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The Certified Quality Engineer Handbook Third Edition

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Also available from ASQ Quality Press:

The Certified Six Sigma Black Belt Handbook, Second Edition T. M. Kubiak and Donald W. Benbow The Certified Quality Inspector Handbook H. Fred Walker, Ahmad K. Elshennawy, Bhisham C. Gupta, and Mary McShane Vaughn The Certified Reliability Engineer Handbook Donald W. Benbow and Hugh W. Broome The Certified Software Quality Engineer Handbook Linda Westfall, editor The Metrology Handbook Jay Bucher, editor The Certified Six Sigma Green Belt Handbook Roderick A. Munro, Matthew J. Maio, Mohamed B. Nawaz, Govindarajan Ramu, and Daniel J. Zrymiak The Quality Calibration Handbook: Developing and Managing a Calibration Program Jay L. Bucher Design of Experiments with MINITAB Paul Mathews The Certified Manager of Quality/Organizational Excellence Handbook, Third Edition Russell T. Westcott, editor The Certified Quality Technician Handbook Donald W. Benbow, Ahmad K. Elshennawy, and H. Fred Walker The Weibull Analysis Handbook, Second Edition Bryan Dodson Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes Stefan H. Steiner and R. Jock MacKay Lean Kaizen: A Simplified Approach to Process Improvements George Alukal and Anthony Manos

To request a complimentary catalog of ASQ Quality Press publications, call 800-248-1946, or visit our Web site at http://www.asq.org/quality-press.



The Certified Quality Engineer Handbook Third Edition

Connie M. Borror, Editor

ASQ Quality Press Milwaukee, Wisconsin



American Society for Quality, Quality Press, Milwaukee 53203 © 2009 by ASQ All rights reserved. Published 2008 Printed in the United States of America 14 13 12 11 10 09 08 5 4 3 2 1 Library of Congress Cataloging-in-Publication Data The certified quality engineer handbook / Connie M. Borror, editor.—3rd ed. p. cm. Includes bibliographical references and index. ISBN 978-0-87389-745-7 (hard cover : alk. paper) 1. Production management—Quality control. 2. Reliability (Engineering). I. Borror, Connie M. TS156.C423 2008 658.4'013—dc22

2008040494

ISBN: 978-0-87389-745-7 No part of this book may be reproduced in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Publisher: William A. Tony Acquisitions Editor: Matt T. Meinholz Project Editor: Paul O’Mara Production Administrator: Randall Benson ASQ Mission: The American Society for Quality advances individual, organizational, and community excellence worldwide through learning, quality improvement, and knowledge exchange. Attention Bookstores, Wholesalers, Schools and Corporations: ASQ Quality Press books, videotapes, audiotapes, and software are available at quantity discounts with bulk purchases for business, educational, or instructional use. For information, please contact ASQ Quality Press at 800-248-1946, or write to ASQ Quality Press, P.O. Box 3005, Milwaukee, WI 53201-3005. To place orders or to request a free copy of the ASQ Quality Press Publications Catalog, including ASQ membership information, call 800-248-1946. Visit our Web site at www.asq.org or http://www.asq.org/quality-press. Printed in the United States of America Printed on acid-free paper



Table of Contents

CD-ROM Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Figures and Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface to the Third Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface to the Second Edition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface to the First Edition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How to Use This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I

Management and Leadership. . . . . . . . . . . . . . . . . . . . . . . . . .

xii xiii xx xxii xxiii xxvi xxviii

1

Chapter 1 A. Quality Philosophies and Foundations . . . . . . . . . . . . . . . . . . . . . History of Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Quality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 2 7

Chapter 2 B. The Quality Management System . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Strategic Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Deployment Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Quality Information System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 10 14 33

Chapter 3 C. ASQ Code of Ethics for Professional Conduct . . . . . . . . . . . . . . . Ethical Dilemmas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 42

Chapter 4 D. Leadership Principles and Techniques . . . . . . . . . . . . . . . . . . . . . Developing, Building, and Organizing Teams . . . . . . . . . . . . . . . . . . . . . . . . . . Leading Quality Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 46 50

Chapter 5 E. Facilitation Principles and Techniques. . . . . . . . . . . . . . . . . . . . . . Facilitator Roles and Responsibilities in the Team Environment. . . . . . . . . . . Idea Processing and Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52 52 55

Chapter 6 F. Communication Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Need for Communication Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Creating a Shared Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Communication Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 59 60

Chapter 7 G. Customer Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Customer Needs and Wants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Function Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Customer-Driven Quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

64 64 65 70

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Chapter 8 H. Supplier Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Procurement Standards and Specifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Survey versus Audit versus Sampling Inspection . . . . . . . . . . . . . . . . . . . . . . . Surveying the Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplier Rating and Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

72 72 73 74 77

Chapter 9 I. Overcoming Barriers to Quality Improvement . . . . . . . . . . . . . . . Twelve Obstacles to Implementing Quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Part I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

80 80 82

Part II

85

The Quality System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 10

A. Elements of the Quality System . . . . . . . . . . . . . . . . . . . . . . . . . .

86

Chapter 11

B. Documentation of the Quality System . . . . . . . . . . . . . . . . . . . . .

90

Chapter 12 C. Quality Standards and Other Guidelines . . . . . . . . . . . . . . . . . . National versus International Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The ISO 9000 Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Some Other Quality Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Malcolm Baldrige National Quality Award . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93 93 94 98 98

Chapter 13 D. Quality Audits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Types of Audits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Roles and Responsibilities in Audits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Audit Planning and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Audit Reporting and Follow-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

100 100 101 102 103

Chapter 14 E. Cost of Quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Economics of Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goal of a Quality Cost System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Management of Quality Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Cost Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Cost Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Cost Summary and Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Cost Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using Quality Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Improvement and Reducing Quality Costs. . . . . . . . . . . . . . . . . . . . . . Quality Cost Principles and Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

105 105 107 107 108 108 111 112 113 113 114 114

Chapter 15 F. Quality Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developing a Training Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Needs Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developing Curricula and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Determine Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Final Thought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Part II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

116 116 117 118 118 119 122 122


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Part III

vii

Product and Process Design . . . . . . . . . . . . . . . . . . . . . . . . . 123

Chapter 16 A. Classification of Quality Characteristics . . . . . . . . . . . . . . . . . . Measurement of Variables Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of Attributes Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

124 124 124

Chapter 17 B. Design Inputs and Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Background and Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Product/Process Development Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

126 126 126 128 129

Chapter 18 C. Technical Drawings and Specifications. . . . . . . . . . . . . . . . . . . . Dimensioning and Tolerancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

131 131

Chapter 19

D. Design Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135

Chapter 20 E. Reliability and Maintainability. . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Predictive and Preventive Maintenance Tools . . . . . . . . . . . . . . . . . . . . . . . . 2. System Reliability and Maintainability Indices . . . . . . . . . . . . . . . . . . . . . . . 3. The Bathtub Curve and Other Failure Models . . . . . . . . . . . . . . . . . . . . . . . . 4. Reliability, Safety, and Hazard Assessment Tools . . . . . . . . . . . . . . . . . . . . . Summary of Part III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 137 137 144 151 156 178

Part IV

179

Product and Process Control. . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 21 A. Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Work Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

180 180 182

Chapter 22 B. Material Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Material Identification, Status, and Traceability . . . . . . . . . . . . . . . . . . . . . . . 2. Material Segregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Classification of Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Material Review Board (MRB). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

185 186 189 190 191

Chapter 23 C. Acceptance Sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling Inspection versus 100 Percent Inspection . . . . . . . . . . . . . . . . . . . . . 1. Sampling Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Sampling Standards and Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variables Sampling Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sequential Sampling Plans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Sample Integrity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193 194 195 205 215 218 224

Chapter 24 D. Measurement and Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Measurement Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Measurement Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Destructive and Nondestructive Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

227 227 228 242


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Chapter 25 E. Metrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction and Scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Context of Measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concepts in Metrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traceability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

245 245 245 250 252 253

Chapter 26 F. Measurement System Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . Terms and Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gage Repeatability and Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Part IV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

256 256 258 271

Part V Continuous Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . .

273

Chapter 27 A. Quality Control Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pareto Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cause-and-Effect Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Check Sheets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Run Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of the Graphical Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

274 275 276 278 281 282 284 285 288 288

Chapter 28 B. Quality Management and Planning Tools . . . . . . . . . . . . . . . . . Affinity Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interrelationship Digraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tree Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Decision Program Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matrix Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prioritization Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activity Network Diagram and Arrow Diagram . . . . . . . . . . . . . . . . . . . . . . . . Process Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Value Chain (PVC) Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value Stream Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SIPOC Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Quality Management and Planning Tools . . . . . . . . . . . . . . . . . .

289 290 292 294 298 299 303 304 308 311 311 312 313 316

Chapter 29 C. Continuous Improvement Techniques. . . . . . . . . . . . . . . . . . . . . Total Quality Management (TQM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaizen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PDSA or PDCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reengineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustaining and Communicating Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The DMAIC Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theory of Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

318 319 319 320 320 321 332 333 334



ix

Lean Enterprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

334

Chapter 30 D. Corrective Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 1: Identify the Opportunity (Problem Identification) . . . . . . . . . . . . . . . Phase 2: Analyze the Current Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 3: Develop the Optimal Solution(s) (Correction). . . . . . . . . . . . . . . . . . . Phase 4: Implement Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 5: Study the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 6: Standardize the Solution (Recurrence Control). . . . . . . . . . . . . . . . . . Phase 7: Plan for the Future (Effectiveness Assessment). . . . . . . . . . . . . . . . . .

346 347 349 350 351 352 352 354

Chapter 31 E. Preventive Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Verifying the Effectiveness of Preventive Actions . . . . . . . . . . . . . . . . . . . . . . . Robust Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Part V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

355 356 356 357

Part VI

Quantitative Methods and Tools . . . . . . . . . . . . . . . . . . . . . 359

Chapter 32 A. Collecting and Summarizing Data . . . . . . . . . . . . . . . . . . . . . . . . 1. Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Measurement Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Data Collection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Data Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Graphical Methods for Depicting Relationships. . . . . . . . . . . . . . . . . . . . . . . 7. Graphical Methods for Depicting Distributions . . . . . . . . . . . . . . . . . . . . . . .

360 360 361 361 363 365 372 374

Chapter 33 B. Quantitative Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Drawing Statistical Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Probability Terms and Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

379 379 380 381

Chapter 34 C. Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Continuous Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Discrete Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Form of Expected Value and Variance. . . . . . . . . . . . . . . . . . . . . . . . . . Central Limit Theorem Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

394 394 408 414 415

Chapter 35 D. Statistical Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Point Estimates and Confidence Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Paired-Comparison Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Goodness-of-Fit Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Analysis of Variance (ANOVA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Contingency Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

418 418 430 453 456 459 469

Chapter 36 E. Relationships Between Variables . . . . . . . . . . . . . . . . . . . . . . . . . 1. Linear Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Simple Linear Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Time-Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

473 473 483 488


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Table of Contents

Chapter 37 F. Statistical Process Control (SPC) . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Objectives and Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Common and Special Causes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Selection of Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Rational Subgrouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Control Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Control Chart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Pre-Control Charts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Short-Run Statistical Process Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

491 491 492 492 493 494 519 522 524

Chapter 38 G. Process and Performance Capability . . . . . . . . . . . . . . . . . . . . . . 1. Process Capability Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Calculating Process Performance versus Specifications . . . . . . . . . . . . . . . . 3. Process Capability Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Process Performance Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

527 527 529 530 532

Chapter 39 H. Design and Analysis of Experiments. . . . . . . . . . . . . . . . . . . . . . 1. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Planning and Organizing Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. One-Factor Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Full-Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Two-Level Fractional Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Part VI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

534 534 538 540 542 549 566 574

Part VII Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

575

Appendix A ASQ Certified Quality Engineer (CQE) Body of Knowledge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Levels of Cognition Based on Bloom’s Taxonomy—Revised (2001) . . . . . . . . . .

576 584

Appendix B Control Limit Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variables Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attributes Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

585 585 585

Appendix C

Constants for Control Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

586

Appendix D Statistical Tolerance Factors for at Least 99 Percent of the Population (“k-Values”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

587

Appendix E Standard Normal Distribution for Selected Z-Values . . . . . . . . .

588

Appendix F Areas under Standard Normal Distribution to the Left of Z-Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

590

Appendix G

F Distribution F0.10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

593

Appendix H

F Distribution F0.05 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

597

Appendix I

F Distribution F0.01. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

601

Appendix J

Binomial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

605

Appendix K

Chi-Square Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

607



xi

Appendix L

Exponential Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

609

Appendix M

Poisson Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

611

Appendix N

Median Ranks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

613

Appendix O

Normal Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

615

Appendix P

Values of t Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

617

Appendix Q Selected National and International Quality System Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

619

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

623 645 655



CD-ROM Contents

The following files are provided on the CD-ROM accompanying this book. Certified Quality Engineer Certification (simulated exam) File name: CQE-SimulatedExam.pdf Eighty questions distributed by topic approximately proportional to those in the CQE Body of Knowledge. Sample Exam Questions File name: CQE-SampleExam.pdf A set of questions from the Body of Knowledge. Acceptance Sampling Tables File name: SamplingTables.pdf Selected tables from ANSI/ASQ Z1.4 and Z1.9. Audio Presentations Folder name: Audio Presentations Presentations of some statistical methods are narrated by the editor.

xii



List of Figures and Tables

Part I Figure 1.1

Deming’s 14 points. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

Table 1.1

Comparing the impact quality can have. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

Figure 2.1

Ten effectiveness tests for strategic quality plans. . . . . . . . . . . . . . . . . . . . . .

11

Figure 2.2

Critical path method (CPM) chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

Figure 2.3

XYZ corporation dashboard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

Figure 2.4

Action plan format example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

Figure 2.5

Action plan implementation schedule example. . . . . . . . . . . . . . . . . . . . . . . .

24

Table 2.1

A typical project planning sequence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28

Figure 2.6

Work breakdown structure (partial). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30

Figure 2.7

Gantt chart example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

Figure 2.8

Information systems strategy matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36

Figure 2.9

The V model for software development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37

Figure 2.10

Current status report example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

Figure 4.1

Linking team structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

Figure 5.1

Nominal group technique ranking table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56

Figure 5.2

Multivoting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57

Figure 7.1

QFD house of quality diagram for a paperwork process. . . . . . . . . . . . . . . .

66

Figure 7.2

Input–output requirements matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67

Figure 7.3

House of quality for a car door. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

68

Table 7.1

Customer perspectives of value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

Figure 10.1

Product lifecycle and quality system elements. . . . . . . . . . . . . . . . . . . . . . . .

87

Figure 11.1

Tiers of the quality documentation hierarchy. . . . . . . . . . . . . . . . . . . . . . . . . .

91

Table 14.1

Quality cost elements by category. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109

Table 15.1

Five different levels of evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

120

Figure 15.1

Training versus education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

122

Figure 16.1

Go/no-go gage to check the diameter of a shaft. . . . . . . . . . . . . . . . . . . . . . .

125

Figure 18.1

Some geometric tolerancing symbols. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

132

Part II

Part III

xiii


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. xiv


Figure 18.2

Interpretation of a geometric tolerance on a drawing. . . . . . . . . . . . . . . . . . .

133

Figure 18.3

Part drawing with and without tolerances of form. . . . . . . . . . . . . . . . . . . . .

133

Figure 18.4

Two parts dimensioned with positional tolerances. . . . . . . . . . . . . . . . . . . . .

134

Table 20.1

Number of failures in the time intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141

Table 20.2

Reliability, cumulative distribution function, failure density, and hazard rate for the light bulb example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141

Figure 20.1

Reliability function versus time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

142

Figure 20.2

Distribution function versus time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

142

Figure 20.3

Failure density versus time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143

Figure 20.4

Hazard rate versus time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143

Figure 20.5

A typical series system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

145

Figure 20.6

A typical parallel system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

147

Figure 20.7

A standby system with n components in standby mode. . . . . . . . . . . . . . . .

148

Figure 20.8

The general failure rate model (the bathtub curve). . . . . . . . . . . . . . . . . . . . .

152

Figure 20.9

Probability density function for constant failure rate. . . . . . . . . . . . . . . . . . .

153

Figure 20.10

Reliability function for constant failure rate. . . . . . . . . . . . . . . . . . . . . . . . . . .

153

Figure 20.11

Probability density functions for the Weibull model with different shape and scale parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155

Figure 20.12 Hazard rate functions for the Weibull model with different shape and scale parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155

Figure 20.13

Blank design FMEA form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

164

Figure 20.14

Blank process FMEA form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

165

Table 20.3

Design FMEA severity criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

167

Table 20.4

Process FMEA severity criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

168

Table 20.5

Design FMEA occurrence criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

169

Table 20.6

Process FMEA occurrence criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

169

Table 20.7

Design FMEA detection criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

170

Table 20.8

Process FMEA detection criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171

Figure 20.15

Design FMEA example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

173

Figure 20.16

Process FMEA example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

174

Figure 20.17

Failure mode effects and criticality analysis. . . . . . . . . . . . . . . . . . . . . . . . . . .

175

Figure 20.18

Simple fault tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

177

Control plan example: page 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

183

Figure 21.2

Control plan example: page 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

184

Table 22.1

Standards pertaining to material review board operations. . . . . . . . . . . . . .

192

Figure 23.1

An operating characteristic (OC) curve for n = 50 and c = 3 .. . . . . . . . . . . . .

196

Table 23.1

Probability of acceptance for various levels of fraction nonconforming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

197

Table 23.2

AOQ levels for various levels of fraction nonconforming. . . . . . . . . . . . . . .

200

Figure 23.2

AOQ curve for: n = 50, c = 3, and infinite lot size. . . . . . . . . . . . . . . . . . . . . . .

201

Part IV Figure 21.1


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. List of Figures and Tables

Figure 23.3

xv

Effect on an OC curve of changing sample size (n) when accept number (c) is held constant. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

201

Table 23.3

Probability of acceptance for various n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

202

Figure 23.4

Effect of changing accept number (c) when sample size (n) is held constant. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

202

Table 23.4

Fraction defective at indifference quality level. . . . . . . . . . . . . . . . . . . . . . . . .

203

Figure 23.5

Effect of changing lot size (N) when acceptance number (c) and sample size (n) are held constant. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

203

Table 23.5

Probability of acceptance for various lot sizes. . . . . . . . . . . . . . . . . . . . . . . . .

204

Figure 23.6

Operating characteristic curves for sampling plans having the sample size equal to 10 percent of the lot size. . . . . . . . . . . . . . . . . . . . . . . . . .

204

Figure 23.7

OC curve for double sampling plan where; n1 = 75, c1 = 0, r1 = 3, n2 = 75, c2 = 3, r2 = 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

207

Figure 23.8

Average outgoing quality curve for double sampling plan. . . . . . . . . . . . . .

208

Figure 23.9

Average sample number curve for double sampling plan. . . . . . . . . . . . . . .

210

Figure 23.10

Switching rules for normal, tightened, and reduced inspection. . . . . . . . . .

212

Figure 23.11

Structure and organization of ANSI/ASQ Z1.9-2003. . . . . . . . . . . . . . . . . . . .

217

Figure 23.12 Decision areas for a sequential sampling plan. . . . . . . . . . . . . . . . . . . . . . . . .

220

Table 24.1

Typical standards and instrumentation for industrial length and angle measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

232

Figure 24.1

ISO/R468 surface roughness parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

235

Figure 24.2

Other parameters of surface roughness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

236

Figure 24.3

Two types of roundness-measuring instruments: (a) rotating table, (b) rotating workpiece. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

237

Figure 24.4

Four ways by which a center may be chosen. . . . . . . . . . . . . . . . . . . . . . . . . .

239

Figure 24.5

Coordinate measuring machine classifications. . . . . . . . . . . . . . . . . . . . . . . .

241

Figure 25.1

Classification of standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

247

Table 25.1

Base units of the international system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

248

Table 25.2

Definitions of the SI base units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

248

Figure 25.2

Factors affecting the measuring process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

251

Table 26.1

Bias and average estimates for parts of different sizes. . . . . . . . . . . . . . . . . .

257

Table 26.2

Necessary quantities for an analysis of variance. . . . . . . . . . . . . . . . . . . . . . .

262

Table 26.3

Typical data for the gage R&R experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . .

263

Table 26.4

Gage R&R estimates using the tabular method. . . . . . . . . . . . . . . . . . . . . . . .

264

Table 26.5

ANOVA for the gage R&R example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265

Table 26.6

Gage R&R results using the ANOVA method. . . . . . . . . . . . . . . . . . . . . . . . .

266

Table 26.7

Variance component estimates for both methods. . . . . . . . . . . . . . . . . . . . . . – X and R control charts for the thermal performance example. . . . . . . . . . . .

267

Figure 26.1

268

Part V Figure 27.1

Four primary flowcharting symbols. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

276

Figure 27.2

Flowchart for diagnostic testing process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

277


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. xvi


Figure 27.3

Typical Pareto chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

278

Figure 27.4

Cause-and-effect diagram/template. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

280

Figure 27.5

Cause-and-effect diagram: bus safety discrepancies. . . . . . . . . . . . . . . . . . .

280

Figure 27.6

A simple check sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

282

Figure 27.7

Histogram of progressive strength of concrete samples. . . . . . . . . . . . . . . . .

284

Figure 27.8

Three possible relationships identified by scatter diagrams. . . . . . . . . . . . .

285

Table 27.1

Training data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

286

Figure 27.9

Training time versus defects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

286

Figure 27.10

Run chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

287

Figure 28.1

Student focus group affinity diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

292

Figure 28.2

Line support subprocess interrelationship digraph. . . . . . . . . . . . . . . . . . . .

294

Figure 28.3

Simplified line shutdown fault tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

296

Figure 28.4

Partial manufacturing cost improvement goal tree. . . . . . . . . . . . . . . . . . . . .

297

Figure 28.5

Receiving/storage/stocking subprocesses PDPC. . . . . . . . . . . . . . . . . . . . . .

300

Table 28.1

Issues, possible root causes, and general impact summary for receiving/storage/stocking PDPC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

301

Figure 28.6

Quality function deployment matrix diagram. . . . . . . . . . . . . . . . . . . . . . . . .

302

Table 28.2

Line support improvement process activities, sequences, and durations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

305

Simplified CPM schedule network–line support improvement implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

306

Figure 28.7 Table 28.3

Line support improvement scheduling details. . . . . . . . . . . . . . . . . . . . . . . .

307

Figure 28.8

Enteprise-level process map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

309

Figure 28.9

Visual alternative–improved subprocess map/PDPC. . . . . . . . . . . . . . . . . . .

310

Figure 28.10

Generic production system process value chain diagram. (a) Analytical view. (b) General systems view. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

312

Value stream map for a manufacturing process. . . . . . . . . . . . . . . . . . . . . . .

313

Figure 28.12 SIPOC diagram for work-related injuries. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

314

Figure 28.11

Figure 28.13 Benchmarking and breakthrough thinking. . . . . . . . . . . . . . . . . . . . . . . . . . .

316

Figure 29.1

Basic plan–do–study–act cycle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

321

Table 29.1

Six Sigma needs checklist. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

323

Figure 29.2

Implication of sigma–quality level. Parts per million (ppm) rate for part or process step, considers a 1.5s shift of the mean where only 3.4 ppm fail to meet specifications at a six sigma quality level. . . . . . . . . . .

327

Figure 29.3

Normal distribution curve illustrates three sigma and six sigma parametric conformance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

328

Figure 29.4

With a centered normal distribution between six sigma limits, only two devices per billion fail to meet the specification target. . . . . . . . . . . . . .

328

Figure 29.5

Effects of a 1.5s shift where only 3.4 ppm fail to meet specifications. . . . . .

329

Figure 29.6

Defect rates (ppm) versus sigma–quality level. . . . . . . . . . . . . . . . . . . . . . . . .

329

Figure 29.7

Six Sigma metrics and implementation strategy. . . . . . . . . . . . . . . . . . . . . . .

332

Figure 29.8

Value stream map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

335

Figure 29.9

A sea of inventory often hides unresolved problems. . . . . . . . . . . . . . . . . . .

338


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. List of Figures and Tables xvii

Figure 29.10

C-shaped manufacturing cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

339

Figure 29.11

A poka-yoke technique ensures that the round and square parts are placed in correct containers. Neither part will fit through the hole in the top of the incorrect container. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

343

Figure 30.1

The seven phases of corrective action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

346

Table 30.1

Positrol of a wave soldering process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

353

Table 30.2

Checklist for process certification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

353

Table 31.1

Types of fail-safe devices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

355

Example of tally or check sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

362

Figure 32.2

Dot plot of blood analysis turnaround times. . . . . . . . . . . . . . . . . . . . . . . . . .

369

Figure 32.3

Histograms of variously-shaped distributions. . . . . . . . . . . . . . . . . . . . . . . . .

370

Table 32.1

Frequency and cumulative frequency distributions for the ungrouped diameter data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

371

Table 32.2

Frequency and cumulative frequency distributions for grouped diameter data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

371

Table 32.3

Categorical frequency distribution of manufacturing defects. . . . . . . . . . . .

371

Figure 32.4

Stem and leaf diagrams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

373

Figure 32.5

Box-and-whisker diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

373

Figure 32.6

Box plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

374

Figure 32.7

Multiple box plot example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

375

Figure 32.8

Normal probability plot of puncture force for toasted corn flakes. . . . . . . .

376

Figure 32.9

Weibull probability plot for life of a part. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

377

Table 33.1

Contingency table of part color and part size. . . . . . . . . . . . . . . . . . . . . . . . . .

385

Figure 34.1

A probability density function for a random variable X. . . . . . . . . . . . . . . . .

395

Part VI Figure 32.1

Figure 34.2

Probability density function (pdf) for the normal distribution. . . . . . . . . . .

396

Figure 34.3

Probability density function for a standard normal distribution. . . . . . . . .

398

Figure 34.4

Probability density function for a standard normal distribution. . . . . . . . .

399

Figure 34.5

Probability density function for an exponentially distributed random variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

400

Figure 34.6

Probability density function for the Weibull distribution. . . . . . . . . . . . . . .

402

Figure 34.7

Continuous uniform probability distribution. . . . . . . . . . . . . . . . . . . . . . . . .

403

Figure 34.8

Several chi-square distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

406

Figure 34.9

Probability density functions for three t distributions. . . . . . . . . . . . . . . . . .

407

Table 34.1

pdf, mean, and variance for certain continuous distributions. . . . . . . . . . . .

408

Table 34.2

pmf, mean, and variance for certain discrete distributions. . . . . . . . . . . . . .

414

Table 35.1

Summary of situations outlined for testing the population mean. . . . . . . .

435

Table 35.2

One-way ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

464

Figure 35.1

Box plots for catalyst amount. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

465

Figure 35.2

Interaction plots of factors A and B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

466

Table 35.3

Two-way ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

467


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. xviii List of Figures and Tables

Table 35.4

ANOVA table for conversion rate example with two factors of interest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

468

Figure 35.3

Interaction plot for temperature and catalyst. . . . . . . . . . . . . . . . . . . . . . . . . .

468

Figure 36.1

Scatter plot of temperature and yield. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

474

Figure 36.2

Scatter plot and fitted regression line for the yield data. . . . . . . . . . . . . . . . .

477

Table 36.1

ANOVA table for testing significance of regression. . . . . . . . . . . . . . . . . . . .

481

Figure 36.3

Various scatter plots for two variables x and y. . . . . . . . . . . . . . . . . . . . . . . . .

485

Figure 36.4

Run chart for NMCM rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

489

Table 37.1

General notation for subgroup data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

495

Table 37.2

Turnaround times for CBC analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x– and R control charts for turnaround times. . . . . . . . . . . . . . . . . . . . . . . . . .

496

Figure 37.1 Table 37.3

497

Figure 37.2

Turnaround times for CBC analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x– and s control charts for CBC analysis turnaround times. . . . . . . . . . . . . .

499 499

Table 37.4

Weights for dry food packages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

501

Figure 37.3

I and MR control charts for package weights. . . . . . . . . . . . . . . . . . . . . . . . . .

502

Table 37.5

Account activities in error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

504

Figure 37.4

p chart for accounts in error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

505

Figure 37.5

Revised p chart for accounts in error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

505

Table 37.6

Surgical site infection rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

506

Figure 37.6

p chart for surgical site infection rate using varying sample sizes. . . . . . . .

507

Figure 37.7

np control chart for accounts in error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

508

Table 37.7

Errors on hospital billing statements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

509

Figure 37.8

c chart for number of billing errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

510

Table 37.8

Billing statement errors for a 24-day period. . . . . . . . . . . . . . . . . . . . . . . . . . .

511

Figure 37.9

u chart for billing statement errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

512

Table 37.9

Weights for dry food packages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

514

Figure 37.10

CUSUM chart for package weight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

515

Figure 37.11

V-mask for package weight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

516

Figure 37.12

EWMA control chart for package weight. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

517

Figure 37.13

Example of a pre-control chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

523

Table 37.10

Number of nonconformities for printed circuit boards. . . . . . . . . . . . . . . . .

525

Figure 37.14

Short-run c chart for printed circuit boards. . . . . . . . . . . . . . . . . . . . . . . . . . .

526

3

Figure 39.1

A 2 full-factorial data collection sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

537

Figure 39.2

A 23 full-factorial data collection sheet with data entered. . . . . . . . . . . . . . .

537

Figure 39.3

A 23 full-factorial data collection sheet with run averages. . . . . . . . . . . . . . .

538

Table 39.1

ANOVA table for a randomized block design. . . . . . . . . . . . . . . . . . . . . . . . .

546

Table 39.2

ANOVA table for the washing solution example. . . . . . . . . . . . . . . . . . . . . . .

547

Table 39.3

ANOVA table for the washing solution example without blocking. . . . . . .

549

Table 39.4

ANOVA table for two-factor factorial experiment. . . . . . . . . . . . . . . . . . . . . .

551

Figure 39.4

All possible combinations of two factors A and B, with two levels each. . .

552

Table 39.5

Partitioning effect on ventilation effectiveness. . . . . . . . . . . . . . . . . . . . . . . .

553


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. List of Figures and Tables

xix

Figure 39.5

Cube plot for partition length, partition height, and gap underneath. . . . .

554

Figure 39.6

Main effects plot for the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . .

555

Table 39.6

Main effect and interaction table of contrasts for the ventilation example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

556

Table 39.7

Estimated effects for the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . .

557

Figure 39.7

Interaction plot for partition length and partition height. . . . . . . . . . . . . . . .

557

Figure 39.8

Interaction plot for partition length and gap underneath. . . . . . . . . . . . . . .

558

Figure 39.9

Interaction plot for partition height and gap underneath. . . . . . . . . . . . . . . .

558

Table 39.8

ANOVA table for the ventilation example. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

559

Table 39.9

ANOVA table for the ventilation example. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

559

Table 39.10

t-tests for factors and interactions for the air quality example. . . . . . . . . . .

560

Figure 39.10

Normal probability plot of the residuals for the air quality example. . . . . .

562

Figure 39.11

Residuals plotted against levels of factor A (partition length). . . . . . . . . . . .

562

Figure 39.12

Residuals plotted against factor B (partition height). . . . . . . . . . . . . . . . . . . .

563

Figure 39.13

Contour plot for the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

563

Table 39.11

A single replicate of the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . .

565

Figure 39.14

Normal probability plot of the estimated effects for the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

565

Table 39.12

t-test results for the air quality example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3–1

566

Table 39.13

Contrasts for a 2

design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

568

Table 39.14

Half-fraction of a 24 factorial design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

569

Table 39.15

Example using signal-to-noise ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

571

Table 39.16

Illustration of inner and outer arrays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

573



Preface to the Third Edition

T

he third edition of The Certified Engineering Handbook was written to provide the quality professional with an updated resource that follows the CQE Body of Knowledge. Today it is not uncommon for the quality engineer to be involved in quality improvement activities in healthcare, finance, education, software applications, and other nonmanufacturing sectors. In response, numerous new examples and illustrations that cover applications in some of these areas are included in this edition. Key features of the third edition include: • New and updated references • Extensive revision of the statistical methods complete with numerous updated examples and illustrations • Over 75 new glossary entries Much of the material in Parts I and II of the second edition has been retained in this edition, with updated references. However, the reader will find an extensive revision of the statistical methods presented throughout the book. Part V and Part VI have been significantly revised with new discussion, definitions, and examples illustrating each of the statistical techniques as they appear in the Body of Knowledge. Portions of Part IV have also been rewritten to reflect advances in methods and applications in quality improvement activities such as conducting gage repeatability and reproducibility studies. The goal in writing the third edition was to provide a handbook that could be used in preparation for the CQE Exam or as a reference text for professional development. When a complete description or discussion of a topic is beyond the scope of the handbook, useful references have been included for further reading. It is our hope that the reader will find the new examples, explanations, and references useful. It is important to recognize that a handbook of this magnitude could not be completed without the dedication of many people. I would like to thank the previous editors, Roger W. Berger, Donald W. Benbow, Ahmad K. Elshennawy, and H. Fred Walker, for their contributions and organization of the material in the first and second editions. In addition, gratitude goes to the authors who contributed to the first edition of the text. They wrote many of the chapters in the first edition, portions of which were included in the second edition. The oversight and production of the third edition was professionally and carefully carried out by Paul O’Mara, Matthew Meinholz, William Tony, and Randy Benson at ASQ, and Leayn and Paul Tabili at New Paradigm Graphics, Inc. xx


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Lastly, I would like to thank Dr. G. Geoffrey Vining and Dr. Douglas C. Montgomery for their efforts in seeing the third edition come to fruition, their careful editing of the new material, and recommendations for presentation of material. They rightfully could have been coeditors of this edition. Connie M. Borror Editor



Preface to the Second Edition

I

n revising The Certified Quality Engineering Handbook our primary goal has been to reflect the changes in the Body of Knowledge for the Certified Quality Engineer that was published by the American Society for Quality early in 2006. We recognize new developments in the quality engineering profession such as: Greater emphasis on communications New problem-solving tools More widespread application of Six Sigma and lean enterprise concepts Revisions to the ISO 9000 standards A need for more examples of how tools are applied to quality problems As Dr. Gregory Watson said in his preface to the first edition, the American Society for Quality has been developing a more strategic perspective of the quality profession and has investigated the implications that current trends across business sectors will have on our profession. The role of quality engineers has continued to shift toward being mentors and trainers for others in using the tools and concepts of quality. Mastering these tools and passing the certification exam are essential steps along the path of becoming recognized as professional quality engineers. The revised edition provides you with both a textbook and a reference book that is completely aligned with the 2006 ASQ Body of Knowledge. The Editors

xxii



Preface to the First Edition

QUALITY ENGINEERING—AN ENDURING PROFESSION Perspective on the Origins of Quality Engineering Quality engineering was one of the disciplines that drove the American Society for Quality (ASQ, formerly known as ASQC) into its existence at the close of the Second World War. ASQ was founded to preserve and expand the expertise acquired in the war. Many improvements in production, statistical application, inspection, and management became standard practice thanks to the ASQ pioneers. From its inception, ASQ emphasized both technical and educational aspects of the quality profession. The first certification program we developed was for quality engineering, and the body of knowledge (BoK) was prepared by a team of educators and practitioners. It was supported by the ASQ General Technical Council and soon became recognized as the core of the emerging science of quality. By creating the CQE and its body of knowledge, ASQ stabilized the meaning of “quality engineer” and also created an operational definition of quality engineering. Over the years this credential has come to mean that the person who possesses it has achieved an objective standard of performance that indicates the ability to perform those tasks required of a quality engineer.

Challenge for Future Quality Engineers Over the past 10 years, the American Society for Quality has been developing a more strategic perspective of the quality profession and has investigated the implications that current trends across business sectors will have on our profession. In 1995 and again in 1999, ASQ took out crystal balls to “study the future” and determine what actions to take in supporting the quality movement and its cadre of professionals. Several trends have been observed in these studies: some are disturbing and others serve as a beacon to warn us to take corrective action in navigating our course into the future. One major implication already observed in many companies is the transference of advanced quality tools from their almost exclusive use by quality professionals into application by frontline managers and their specially trained problem solvers. This trend will challenge quality engineering professionals in two major ways. First, while we observe that quality tools are being disseminated to the masses, this cascade may or may not involve quality professionals. This wider application xxiii


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of advanced statistical methods and quality tools requires quality professionals to accept new roles as technical mentors to the managers of our organizations. This challenge requires each of us to develop a new approach to leadership and to be the catalyst that aids in the dissemination of these methods by finding ways to encourage the proper use and application of these tools. Second, with more and more managers knowing the same tools that we use, we cannot afford to be amateurs in the use and application of advanced quality methods. In order to earn the right to serve as the technical advisors to this next generation of more enlightened managers, all quality professionals must not only seek training in the more advanced technical methods but also must become the masters of these tools and be perceived as such by senior managers.

Call to Continuous Learning and Personal Excellence Rather than giving up on the viability of our profession, this challenge is a call for an even higher commitment to professional performance. In the quality profession, our tradition has been to use independent certifications as evidence of personal mastery of a particular body of knowledge. The achievement of certification as a quality engineer through the ASQ CQE examination is a distinction of professional achievement that represents personal mastery of the basic quality tools and analytical methods. The certified quality engineer is exposed to increased professional opportunity, promotion potential, and salary increases. Most CQEs go on to further develop skills as quality trainers, facilitators, business managers, auditors, applied statisticians, and technical specialists. For all of these career potentials the CQE certification serves as a mark of professionalism that proclaims a readiness to meet new levels of professional challenge and extend knowledge into more complex and difficult areas to master.

Significance and Meaning of Certification There is an old story of a young man who served as an apprentice, passed the tests and skill demonstrations as a journeyman, and was ready to be named an independent tradesman. He went to his master craftsman and told him that he was ready to go out and establish his own practice. The master said he had one more test to pass. The young man replied: “I am ready.” The master asked him to describe the true meaning of his professional credential. The young man immediately replied: “It means the end of my journey, a well-deserved reward for all of my hard work.” The master said that he did not have the right perspective. After a month the young man returned saying that he was ready to answer the question. Again the master asked him the true meaning of his professional credential. This time the young man replied: “It is a symbol of distinction and a sign of high achievement.” Again the master was dissatisfied, and said, “Return to me next month when you understand the full meaning!” In humility, the young man returned after a third month. The master again asked his question and the young man replied: “This credential only represents the beginning. It is the start of a never-ending journey of work, discipline, and a ceaseless commitment to continuous learning.” The master said: “Now you are ready to work on your own!”


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Studying for Self-Improvement As markets become more and more competitive, companies will need to enhance their agility in order to provide a flexible response to changing customer demands. This trend will require more customer intimacy as companies seek to understand the value proposition required by the market and define what customer requirements will deliver the most value to the market that they choose to serve. Quality professionals will be asked to develop real-time quality monitoring systems and data collection and analysis methods that provide corrective feedback to minimize waste, reduce defects, and improve cost-effectiveness of inventory and capital equipment. Quality professionals also will be asked to build systems for monitoring customer behavior, and to use the information in defining better product designs. Quality will become more and more fundamental in the management of routine business operations. Preparation for this emerging trend will call for personal dedication to developing oneself as not only a competent technician, but also as a local leader capable of influencing others to achieve quality performance results in a wide variety of applications. Enhancing professional competence is the starting point to prepare yourself to be a force in this field. The certified quality engineer credential is a big step in the right direction toward personal development and assuring the continued viability of your set of professional skills. This handbook will guide you through the recently updated body of knowledge and provide you with an exceptionally relevant textbook in your preparation for taking the CQE examination. Gregory H. Watson Past President, American Society for Quality (2000–2001) Fellow, American Society for Quality Certified Quality Engineer, American Society for Quality



How to Use This Book

Q

uality engineering is recognized as a core technical discipline in a variety of different industries and functions. The effective quality engineer must understand numerous concepts and techniques. To foster and recognize such achievement, the American Society for Quality Control (as it was then called) created a Certified Quality Engineer (CQE) program in 1967 and has updated it frequently since then. This handbook and the certification program are both offered by ASQ as ways to maintain and stimulate the profession of quality engineering. This book has two main uses: as a learning tool and as a reference tool. The editors have kept these two different uses in mind as we assembled the various parts of the book.

LEARNING TOOL When you are in the learning mode, you first need to see the big picture and then fill in details. You seek continuity, rationale, and examples. Following the ASQprescribed body of knowledge (BoK), we have organized the subject into six broad categories: 1. Management and Leadership 2. The Quality System 3. Product and Process Design 4. Product and Process Control 5. Continuous Improvement 6. Quantitative Methods and Tools Each of these sections has an introduction and summary to give a broad picture of how the details fit together. There are 82 elements in the CQE BoK, and each element is highlighted at the beginning of the pertinent section. Keep in mind that the book was not written as a study guide to pass the certification exam, but as a comprehensive guide to the field of quality engineering. Therefore, most of the sections include material that goes well beyond the CQE exam requirements. If you are using this book to study for the exam you must carefully examine the wording of the BoK to see which topics are of most immediate concern. Some of the more technical material must be studied intensely and repetitively before it is fully grasped. Examples are often essential to complete the learning process, and we have therefore provided many. We also recognize that often xxvi


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xxvii

your thirst for knowledge cannot be satisfied by the contents of just this one book, so we have listed many sources of additional information.

REFERENCE TOOL When you are in the reference-using mode, your thought process is quite different. You want information and you want it quick. Often a single fact, procedure, or definition is required. Regardless of the kind of information you seek, the best starting point is the index. The editors and production staff have greatly extended the index of this second edition, and we recommend you use it regularly to look things up. Several other features serve your reference needs. Immediately following the main text are the necessary statistical tables, all of which are cited in the text. Once familiar with a given statistical tool, you can often use the appropriate table without consulting the chapter. Statistical tables are listed both in the Table of Contents (front matter) and immediately preceding Appendix A (back matter). All figures and tables in the chapters are listed in the front matter, immediately following the Table of Contents. Consulting these lists may lead you to a key answer in certain cases. An extensive glossary provides another reference tool. These definitions come from a variety of sources, including the fourth edition of the Quality Press’s Glossary and Tables for Statistical Quality Control. The editors believe that you will find this book a valuable learning and reference tool. But you are the final judge of our success, so we welcome your comments and suggestions. Please e-mail, phone, or mail using the contact information located on the back cover and copyright page.



Acknowledgments

W

e would like to acknowledgment the following authors and contributors to the previous editions of this book:

Martha Atkins Dennis Arter Andy Barnett Dale H. Besterfield Forrest W. Breyfogle III Elsayed A. Elsayed Hugh Jordan Harrington Bradley Jones William Kolarik Kreg Kukor Becki Meadows Roderick A. Munro Duke Okes Jack B. ReVelle Denise Robitaille David Shores Galal Wehaba Russ Wescott Chris White

In addition, we are grateful to Tae-Yeon Cho, Busaba Laungrungrong, Eric M. Monroe, and Dr. Rong Pan for their review of the examples and calculations, and also to John A. Bringer Jr., for his assistance on some technical material.

xxviii



Part I Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9

A. Quality Philosophies and Foundations B. The Quality Management System C. ASQ Code of Ethics for Professional Conduct D. Leadership Principles and Techniques E. Facilitation Principles and Techniques F. Communication Skills G. Customer Relations H. Supplier Management I. Overcoming Barriers to Quality Improvement

Part I

Management and Leadership

The two main themes of Part I are a broad perspective on the quality profession and the human element in quality. Areas such as strategic planning and leadership may require additional training and years of experience before full competency is achieved. In the same vein, developing communication skills and removing barriers to quality improvement could be callings of a lifetime. After a careful study of this chapter, you will have a clear idea of the elements upon which the profession of quality engineering is based.

1



Part I.A

Chapter 1 A. Quality Philosophies and Foundations

Explain how modern quality has evolved from quality control through statistical process control (SPC) to total quality management and leadership principles (including Deming’s 14 points), and how quality has helped form various continuous improvement tools including lean, Six Sigma, theory of constraints, and so on. (Remember) Body of Knowledge I.A

HISTORY OF QUALITY The quality profession has a long history, which has greatly accelerated over the last 80 years. Joseph M. Juran (1988) has traced the practice of the quality profession back to the ancient Egyptians and the building of the pyramids. For centuries, quality was intrinsically associated with craftsmanship, and each craftsman controlled all aspects of the final product of his craft. This changed dramatically with the Industrial Revolution. Modern quality practices originated in two stages: mass inspection in the early 1900s and the control chart around 1930. Mass inspection became commonplace as a result of Frederick Taylor’s Scientific Management. Workers stopped checking the quality of their work and instead passed it on to specially trained inspectors. Although inspection is a vital element of quality, Walter Shewhart’s invention of the process control chart really initiated the quality profession. Awareness of worker motivation and attitudes as contributors to quality became prevalent in the early 1930s as a result of Elton Mayo’s Hawthorne studies for Western Electric. The next big push for quality emerged during World War II when suddenly peoples’ lives could be destroyed by poor-quality products. At the same time hundreds of American companies were called upon to manufacture goods to the most exacting requirements. Many quality control techniques, such as

2


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3

Part I.A

acceptance sampling and process control charts, which were merely encouraged before the war, became mandatory as part of the defense effort. Two of the leading practitioners of the quality profession—W. Edwards Deming and Joseph M. Juran—established their professional credentials during this time. Both later went to Japan to teach the defeated nation statistical and management tools. In the 1970s it became apparent that the Japanese had learned their lessons well: Americans, the former masters, made repeated trips to the Japanese, the former students, to explore Japanese successes and to bring home proven Japanese methods. The American Society for Quality Control, now known as the American Society for Quality (ASQ), was born soon after World War II when Martin Brumbaugh saw that great benefits would be attained if he could unify various local quality control societies into one national organization. As he struggled with this task, he recognized the superb skills of George Edwards, who was then head of inspection engineering at Bell Telephone Laboratories. Edwards became the first president of the society and helped establish policies that guide its operation to this day. The first three awards the society created to recognize these three pioneers of quality were the Brumbaugh Award, the Shewhart Medal, and the Edwards Medal. In time, the society created numerous other awards, each honoring a specific hero of the profession and recognizing outstanding achievement in a particular area of the profession.

Walter A. Shewhart The industrial age was approaching its second century when a young engineer named Walter A. Shewhart altered the course of industrial history by bringing together the disciplines of statistics, engineering, and economics. He referred to his greatest achievement, the invention of the process control chart, as “the formulation of a scientific basis for securing economic control.” The Shewhart control chart is now sometimes referred to as a process behavior chart. Shewhart wanted statistical theory to serve the needs of industry. He exhibited the restlessness of one looking for a better way. A man of science who patiently developed his and others’ ideas, he was an astute observer of the world of science and technology. While the literature of the day discussed the stochastic nature of both biological and technical systems, and spoke of the possibility of applying statistical methodology to these systems, Shewhart actually showed how it was to be done. In that respect, the field of quality control can claim a genuine pioneer in Shewhart. His book Economic Control of Quality of Manufactured Product, published in 1931, is regarded as a complete and thorough exposition of the basic principles of quality control. Called upon frequently as a consultant, Shewhart served the War Department, the United Nations, the government of India, and others. He was active with the National Research Council and the International Statistical Institute. He was a fellow of numerous societies and in 1947 became the first honorary member of the American Society for Quality. Many consider the Shewhart Medal, given for outstanding technical contributions to the quality profession, to be by far the most prestigious award the American Society for Quality offers.


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Part I: Management and Leadership

Part I.A

W. Edwards Deming and Joseph M. Juran The impact of the Bell Telephone System on the quality profession is almost beyond belief. Shewhart, Edwards, Juran, and Deming all worked for and learned from the Bell System in one way or another. Edwards and Shewhart retired as Bell System employees. Both Juran and Deming went on from the Bell System to become world-famous consultants and authors. Deming became the best-known quality expert in the United States. He delivered his message on quality not only throughout the United States but also around the world. In recognition of his valuable contribution to Japan’s post-war recovery, the Union of Japanese Scientists and Engineers established an annual award for quality achievement called the Deming Prize. Deming (1982) emphasized that the keys to quality are in management’s hands—85 percent of quality problems are due to the system and only 15 percent are due to employees. The heart of his quality strategy is the use of statistical quality control to identify special causes (erratic, unpredictable) and common causes (systemic) of variation. Statistical tools provide a common language for employees throughout a company and permit quality control efforts to be widely diffused. Each employee assumes considerable responsibility for the quality of his or her own work. Those in traditional quality control functions are then able to take more proactive roles in the quality improvement effort. Deming introduced statistical quality control to the Japanese in the early 1950s when Japan was recovering from World War II and trying to overcome a reputation for shoddy workmanship. Deming’s guidance was instrumental in transforming “made in Japan” from a liability to an asset. Deming asserted that there was no point in exhorting employees to produce higher-quality work because the changes needed to improve quality were almost always outside of the workers’ control, such as having the right tools, training, and materials. Instead, management had to accept responsibility for quality. Based on his experience, Deming developed a 14-point set of requirements called Deming’s 14 points, shown in Figure 1.1. He also described seven deadly diseases of the workplace, including emphasis on short-term profits, use of personnel performance evaluations, which he labeled “management by fear,” and mobility of management (that is, management as a profession independent of the product/service or commitment to the organization). Juran, like Deming, built his quality reputation in America and then took his expertise to Japan in the 1950s. The two complemented each other well in Japan, as Deming showed the use of statistical tools and Juran taught the techniques of managing for quality. Juran originated the concept of “the vital few” and the “useful (originally ‘trivial’) many,” which he labeled the Pareto principle, now enshrined in the well-known Pareto diagram. An economist, Vilfredo Pareto, had noticed the phenomenon but it was Juran who applied it to quality improvement. Juran recognized that to improve quality requires a completely different approach from what is needed to maintain existing quality. He demonstrated this idea in his book Managerial Breakthrough, first published in 1964, and later condensed his ideas into the Juran trilogy:



5

1. Quality control: monitoring techniques to correct sporadic problems (analogous to special causes)

3. Quality planning: an annual quality program to institutionalize managerial control and review

Part I.A

2. Quality improvement: a breakthrough sequence to solve chronic problems (analogous to common causes)

Juran served the quality profession well when in 1951 he created the monumental Juran’s Quality Handbook, now in its fifth edition. Juran’s contributions are extensive and varied. He defined quality as “fitness for use by the customer.” He emphasized

1. Create consistency of purpose toward improvement of products and services, with a plan to become competitive and to stay in business. Decide to whom top management is responsible. 2. Adopt the new philosophy. We are in a new economic age. We can no longer live with commonly accepted levels of delays, mistakes, defective materials, and defective workmanship. 3. Cease dependence on mass inspection. Require instead statistical evidence that quality is built-in to eliminate need for inspection. Purchasing managers have a new job and must learn it. 4. End the practice of awarding business on the basis of price tag. Instead, depend on meaningful measures of quality, along with price. Eliminate suppliers who cannot qualify with statistical evidence of quality. 5. Find problems. It is management’s job to work continually on the system (design, incoming materials, composition of material, maintenance, improvement of machines, training, supervision, retraining). 6. Institute modern methods of training on the job. 7. Institute modern methods of supervision of production workers. The responsibility of foremen must be changed from sheer numbers to quality. Improvement of quality will automatically improve productivity. Management must prepare to take immediate actions on reports from foremen concerning barriers such as inherited defects, machines not maintained, poor tools, fuzzy operation definitions. 8. Drive out fear, so that everyone may work effectively for the company. 9. Break down barriers between departments. People in research, design, sales, and production must work as a team, to foresee problems of production that may be encountered with various materials and specifications. 10. Eliminate numerical goals, posters, and slogans for the workforce, asking for new levels of productivity without providing methods. 11. Eliminate work standards that prescribe numerical quotas. 12. Remove barriers that stand between the hourly worker and his right to pride of workmanship. 13. Institute a vigorous program of education and retraining. 14. Create a structure in top management that will push every day on the above 13 points.

Figure 1.1

Deming’s 14 points.



Part I.A

6


the need for top managers to become personally involved in order for a quality effort to be successful and for middle and lower-level managers to learn the language and thinking of top management—money, for example—in order to secure their involvement. Juran’s universal process for quality improvement requires studying symptoms, diagnosing causes, and applying remedies. He repeatedly emphasized that major improvement could be achieved only on a project-by-project basis. The basis for selecting projects was return on investment, now a major component of Six Sigma.

Modern Developments In the six decades since World War II ended, great quality leaders have emerged. Besides those mentioned previously, the following individuals have become famous for their contributions. Philip Crosby popularized the concept of zero defects and established the Crosby Quality College. Kaoru Ishikawa, who helped sponsor Deming’s seminars in Japan, created quality circles and invented the cause-andeffect diagram, also called the Ishikawa diagram. Armand Feigenbaum coined the phrase total quality control and tirelessly preached its fundamentals around the world. Genichi Taguchi, a Japanese engineer, developed a unique system for designing industrial experiments. Eliyahu Goldratt created an improvement system built around the phrase theory of constraints. Other notable contributors to the profession include George Box, Eugene Grant, Jack Lancaster, Frank Gryna, Richard Freund, and Dorian Shainan. Kaizen, a Japanese word that translates roughly into English as improvement, means that workers perform consistent, gradual improvements as they do their regular jobs. The goals of kaizen include the elimination of waste (defined as activities that add cost but do not add value), just-in-time delivery, production load leveling of amount and types, standardized work, paced moving lines, right-sized equipment, and others. Its application is not limited to quality, but quality professionals have effectively applied it. When done correctly it humanizes the workplace, eliminates hard work (both mental and physical), and teaches people how to use the scientific method and to detect waste. Some companies have created a spin-off called kaizen blitz. This is a carefully orchestrated intensive activity designed to produce a significant improvement quickly. Theory of constraints (TOC) has become a popular catchphrase for a system improvement program. It is based on the principle that one—and often more than one—specific factor or element constrains, or prevents, the system from reaching a more desirable state of existence. Goldratt had an insight: managing a complex system or organization can be made both simpler and more effective by providing managers with a few specific areas on which to focus, maximizing performance in the areas of key constraints, or elevating the constraints, making them less constraining. This leads to a view of the company where the constraint guides all strategic decisions. Goldratt’s clients and students have claimed numerous major successes in applying his concepts. He co-authored The Goal, the first famous business novel that informed and entertained many thousands of managers and engineers as it showed the path to success by applying his concepts. TOC, some-



7

Part I.A

times referred to as constraint management, is being actively developed by a loosely coupled community of practitioners around the world. Lean philosophy is exemplified by its terse name: get the job done as simply as possible. It was originally called lean manufacturing but has migrated into many different service industries. A good example of lean philosophy is just-in-time (JIT), where a process is managed so that parts arrive just prior to their actual insertion into the assembly. Another is the famed 5S system, which teaches the benefits of keeping the workplace clean, avoiding waste, and so on. A final example of lean is visual management, which means to post as much information as possible about processes’ requirements, progress, successes, and failures in prominent places where people can see it at a glance, without having to open a notebook or rely on word-of-mouth transmittal. Six Sigma is the final quality philosophy mentioned in this section, but this is not the final time it will be mentioned. Six Sigma has combined and exploited the strengths of the other approaches to the extent that it now dominates all the others. There are journals, conferences, study groups, and consulting firms devoted solely to Six Sigma. Six Sigma combines effective communications, organization of effort, financial accountability, and strong techniques to enable organizations to make sustained improvements over a period of time. Improvements such as cost reduction, quality improvement, cycle time reduction, improved morale, greater profits, and so forth, are all attainable through Six Sigma, but these improvements require a great deal of dedicated work, dedication to the process, and continuous training and learning. See Chapter 29 for more about Six Sigma.

WHAT IS QUALITY? Quality means different things to different people and in different situations. This list gives some of the informal definitions of quality: • Quality is not a program; it is an approach to business. • Quality is a collection of powerful tools and concepts that are proven to work. • Quality is defined by customers through their satisfaction. • Quality includes continual improvement and breakthrough events. • Quality tools and techniques are applicable in every aspect of business. • Quality is aimed at perfection; anything less is an improvement opportunity. • Quality increases customer satisfaction, reduces cycle time and costs, and eliminates errors and rework. • Quality is not just for businesses. It works in nonprofit organizations such as schools, healthcare and social services, and government agencies.




Part I.A

Table 1.1

Comparing the impact quality can have.

99.74% Good = 3 Sigma

99.9998% Good = 6 Sigma

20,000 lost articles of mail per hour

Seven articles lost per hour

Unsafe drinking water for almost 15 minutes each day

One unsafe minute every seven months

5000 incorrect surgical operations per week

1.7 incorrect operations per week

Two short or long landings at most major airports each day

One short or long landing every five years

200,000 wrong drug prescriptions each year

68 wrong prescriptions per year

No electricity for almost seven hours each month

One hour without electricity every 34 years

Results—performance and financial—are the natural consequence of effective quality management. Table 1.1 compares the consequences and impact of quality management at two different quality levels, three sigma and six sigma.

Formal Definitions of Quality The above definitions show that quality is difficult to define, and no one definition can be all-inclusive. The word quality is highly nuanced, and allows many interpretations. For example, a popular online dictionary defines quality as “an inherent or distinguishing characteristic” (http://dictionary.reference.com/search?q= quality); this definition is only the first of ten distinctively different definitions from the same authority. The reader quickly comes to realize that most of the definitions are quite specialized and not really pertinent to the practice of quality engineering. It gets worse: the quality page on Dictionary.com lists specialized meanings from several other authorities, each with a little different twist. One of these meanings, from the ISO 8402 standard, is simultaneously more comprehensive, more explicit, and more authoritative: Quality: the totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs. Not to be mistaken for “degree of excellence” or “fitness for use” which meet only part of the definition. (ISO 8402) This definition is really quite interesting, first because it is published by ISO, an international standards organization, and second because it specifically rebuts the definition that Joseph Juran used throughout his career, “quality = fitness for use.” In contrast, Philip Crosby used the definition, “quality = conformance to specifications,” which is also narrower than ISO 8402. So here you have four quite different definitions and each can be successfully defended as the best in the right situation. There probably never will be an ultimate definition of this all-important word, as the definition is constantly evolving.



9

The views of eight well-known quality experts appear in the July 2001 issue of Quality Progress. Although these experts differ on details and nuances, some common themes appear in all their different quality philosophies:

2. Top management commitment, knowledge, and active participation are critical.

Part I.A

1. Quality improvement is a never-ending process.

3. Management is responsible for articulating a company philosophy, goals, measurable objectives, and a change strategy. 4. All employees in the organization need to be active participants. 5. A common language and set of procedures are important to communicate and support the quality effort. 6. A process must be established to identify the most critical problems, determine their causes, and find solutions. 7. Changes in company culture, roles, and responsibilities may be required.



Part I.B.1

Chapter 2 B. The Quality Management System

T

he quality management system (QMS) will be viewed in three parts: strategic planning, deployment of the strategy, and the information system for monitoring, analyzing, and improving the deployment. The difference between strategic planning and deployment of the strategy can be understood this way: • Strategic planning means deciding what to do. • Deployment means using the best methods to carry out the strategic plan.

1. STRATEGIC PLANNING Identify and define top management’s responsibility for the QMS, including establishing policies and objectives, setting organization-wide goals, and supporting quality initiatives. (Apply) Body of Knowledge I.B.1

Strategic planning usually begins with an analysis phase. The strengths and weaknesses of the organization are assessed and forecasts are generated to predict how market opportunities and competitive threats will change during the time period covered by the study. This analysis is sometimes called a SWOT (the acronym for strengths, weaknesses, opportunities, and threats) study. Ideally, strategic planning for quality will address each aspect of the SWOT analysis. The strengths of the organization can be leveraged to create or sustain competitive advantage. The weaknesses of the organization should be addressed through appropriate measures such as training initiatives to develop strategic skills or process improvement efforts. The opportunities available to the organization can be identified through various marketing research techniques. Key outputs of the marketing research may include estimates of the size and growth rate 10


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 2: B. The Quality Management System

11

Part I.B.1

of the market and clearly articulated customer expectations, desires, and perceptions. This information should drive new product development efforts. Finally, the business environment should be assessed, with particular emphasis on potential threats to the success of the organization. Threats can come from direct competitors offering similar products, indirect competitors offering substitute products or services (butter versus margarine), suppliers of critical proprietary components, and even from distributors who can influence the purchase decisions of the final customers. After the SWOT analysis is complete, the organization can develop strategic quality plans. As the strategy is being formulated, management should evaluate whether the plans will ensure the success of the organization. To discern the effectiveness of strategic quality plans, management should employ a series of sequentially ordered effectiveness tests, shown in Figure 2.1 and discussed following in more detail. 1. The strategy should address all four elements of the SWOT analysis. Leverage the organization’s strengths; remedy the weaknesses. Exploit the opportunities in the market; minimize the potential impact of external threats. It also may be prudent to prepare contingency plans that can be implemented quickly in response to threatening competitor actions. It is crucial for this stage of the planning process to be data-driven. The analysis should be comprehensive, including product quality, finance, purchasing, human resources, marketing and sales, delivery, customer service, and the internal processes that drive these activities. The notion that quality improvement is limited to the factory floor is obsolete. When management begins to apply quality disciplines and statistical methods to assess advertising campaigns and HR initiatives, the transformation is under way. The organization is poised to establish strategic quality plans.

Strategic Planning Effectiveness Tests 1. Does the plan adequately address strengths, weaknesses, opportunities, and threats (SWOT)? 2. Will the plan result in a significant competitive advantage in the marketplace? 3. Is this advantage sustainable? 4. Does the vision statement inspire a sense of mission and purpose among employees? 5. Are the goals and objectives SMART (specific, measurable, achievable, realistic, and time-based)? 6. Are the goals and objectives aligned throughout the organization? 7. Have adequate resources been allocated to achieve the plan? 8. Are organizational structures, systems, and processes appropriate to execute the plan? 9. Is a review/reporting system in place to monitor the execution of the plan? 10. Does the strategic planning team include representatives from all key stakeholders?

Figure 2.1

Ten effectiveness tests for strategic quality plans.



Part I.B.1

12


2. A strategic plan is useful only insofar as it creates a significant competitive advantage in the marketplace. Incremental improvements in quality may not be sufficient to ensure success. Furthermore, the advantage must be recognized and valued by the customer. Engineering and manufacturing can create superior products but that may not help the organization succeed if the customers do not know about the products. Other activities must be involved in the strategic planning process. For example, marketing is responsible for raising customer awareness of product enhancements and influencing purchase decisions through advertising or promotions. Keep in mind that the current strengths of an organization may only generate passing interest among customers. For example, a product may have bestin-class durability but customers may be more interested in appearance, availability, or ease of use. In such cases, consider strategic initiatives that will strengthen the organization’s ability to maximize customer satisfaction throughout the purchase and ownership experience. Such market research tools as conjoint analysis and the Kano quality model can measure how product or service features influence customer purchase behavior. Companies that use market research to help select targets for creating a competitive advantage are more likely to thrive in the marketplace. 3. Is the competitive advantage sustainable? Can your competitors quickly and easily imitate your strategy? Will they respond with counteroffensives that weaken your position? Will your competitors’ strategic efforts pay off a year from now and undermine your leadership in the market? Some consultants recommend avoiding cost reduction as a primary strategy because price is one of the easiest things to imitate in the market. Both you and your competitors will lose if a price war erupts. Anyone can reduce costs by using cheaper components or reducing staff in service or support activities. The risk of this approach is that customers may perceive deteriorating quality, damaging the organization’s reputation and resulting in lost sales. Insisting on a strategy that will deliver outstanding quality through continuous improvement is much more likely to generate a sustainable competitive advantage. The growing popularity of the Six Sigma movement and its impressive success stories demonstrate that it is possible to embark on a major, strategic quality improvement initiative and reap substantial benefits on the bottom line. 4. Does the vision inspire and motivate your employees? The vision should be customer-focused and provide a clear, succinct view of the desired future state of the organization. A major strategic effort will require dedication and commitment. Resources may be stretched to achieve the vision. If the vision is too difficult to achieve, employees may become discouraged and give up. If the vision is too easy to achieve, your competitors may implement something better, and you will be playing catch-up. 5. Goals and objectives are established to direct the efforts of the organization and measure whether the vision is being achieved. The goals and objectives should be SMART, that is, they should be: Specific. State what is expected in precise terms. Measurable. Demonstrate progress through quantitative rather than qualitative or subjective measures.



Chapter 2: B. The Quality Management System

Achievable. The goal can be achieved with available resources if appropriate actions are taken.

Part I.B.1

Realistic. A reasonable, sensible person would accept the goal after considering the degree of difficulty and the probability of success. Time-based. Deadlines serve a useful purpose. Studies have shown that companies who are first to market with new innovations frequently enjoy a significant, sustainable advantage over their competitors. 6. Goals and objectives must be in harmony with each other. As goals are cascaded through an organization and broken down into manageable tasks to be performed by various departments or individuals, unity of purpose and alignment of priorities must be maintained to avoid conflicts. 7. Are resources (staffing, equipment, financing, and so on) adequate to achieve the plan? Can the additional workload be absorbed? Are the skill levels of the employees sufficient? Has the time line been reviewed by affected participants to ensure that there are no scheduling conflicts? Project management techniques such as critical path method (CPM) may be helpful. CPM (see Figure 2.2) will identify the critical paths in the program and provide documentation as to when the resources will be required. 8. Are organizational structures, systems, and processes suitable for executing the plan? Is a departmental reorganization necessary to streamline the flow of work and facilitate concurrent activities? Is an R&D effort necessary to upgrade designs or manufacturing equipment capability? 9. Is a review and reporting system in place to periodically assess progress? These reviews should be conducted by management at a high enough level within 78-week quality management system project 1

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Figure 2.2

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Critical path method (CPM) chart.



Part I.B.2

14


the organization to marshal additional resources as needed when the program is in danger of falling behind schedule. Key program milestones should have clearly defined expectations to ensure consistency and excellence in the execution of the activities. Checklists are a simple yet effective means of communicating the expectations. 10. Does the strategic planning team include the participation of experienced professionals from all affected work groups? Does the team fully understand the strategy, and have they bought into it? The benefits of a cross-functional planning effort cannot be overemphasized. Consider an analogy to the product development process: manufacturing personnel contribute expert advice during the early stages of product design and thereby avoid costly, time-consuming delays and redesigns. Ford Motor Company’s advanced quality planning process lists the use of a cross-functional team as the number one expectation for executing many of the quality disciplines within a product development effort. The importance of establishing the right strategy is critical to the success of an organization. Countless years of sincere toil have been wasted implementing poorly developed strategies. Excellent execution will not assure success unless the plan also is excellent. Juran argues that a structured planning process results in products that perform better and have a shorter development cycle from concept to customer (Juran and Godfrey 1999). Management must explore strategic quality initiatives that go beyond mere incremental improvement. Drive the philosophy of continuous improvement throughout the organization and create a culture of innovation. Look beyond the factory floor for breakthroughs in all systems, such as research and development (R&D), product development, marketing, human resources, and purchasing. Strive for quality initiatives that add value for the customer and establish a sustainable competitive advantage.

2. DEPLOYMENT TECHNIQUES Define, describe, and use various deployment tools in support of the QMS: benchmarking, stakeholder identification and analysis, performance measurement tools, and project management tools such as PERT charts, Gantt charts, critical path method (CPM), resource allocation, and so on. (Apply) Body of Knowledge I.B.2

Quality improvement does not just happen. It must be planned, supported, and monitored just as any other process. Planning requires ways to identify the



15

specific initiatives to be taken on, while support and monitoring require methods for tracking and communicating progress.

Policies provide direction to guide and determine present and future decisions. They indicate the principles to be followed or what is to be done but not specifically how it is to occur. For example, a quality policy should summarize the organization’s view on the meaning and importance of quality as it relates to competitiveness, customers, suppliers, employees, and continual improvement. To ensure consistency and understanding throughout the organization, policies need to be integrated with the strategic plan, then deployed through appropriate initiatives and performance checks. Projects must be justified and scheduled. Performance must be measured and reported. An organization’s policies should be actionable. Some situations may call for temporary adaptation of the policy to meet unanticipated needs. A documented and deployed quality policy provides:

Part I.B.2

Policy Deployment

• A written guide to managerial action, lending stability to the organization • Consideration of quality problems and their ramifications • A basis for auditing practices against policy Deployed policies cascade throughout the organization, directly impacting each functional area and indirectly affecting events, activities, and outcomes depending on those functions. If policies do not have this effect, they are not fulfilling their purpose. Each function and person impacted by the organization’s policy must align their objectives and procedures to support the policy.

Goals and Objectives Simply establishing goals is not enough. Goals must be supported by measurable objectives that are in turn supported by action plans that delineate how the objectives are to be achieved, by when, and by whom. There must be measurable objectives in order to know what the projected results should be and as the means for measuring attainment of those objectives. Similarly, action plans provide more specific information about attaining objectives. An example of the hierarchical relationships between strategy, a goal, objectives, and action plans follows: Organization strategy: Continually build and retain a loyal customer base. Organizational goal: Deliver all products to all customers 100 percent on time. Organizational objective: Given current capacity, improve delivery dates of all future customer orders from 35 percent to 75 percent on-time delivery by February 2010 and to 100 percent by August 2010. Functional objectives: The quality department will assign a quality engineer to convene a cross-functional process improvement team by November 1, 2009. The team will utilize lean manufacturing techniques




Part I.B.2

to reduce cycle time and will continue its efforts until the production process has achieved 100 percent on-time delivery performance. Action plans: Detailed plans state how, when, and by whom the objective will be achieved. Action plans may resemble mini project plans or may be more complex project planning documents as needs dictate. In either case, action plans influence planning and scheduling.

Benchmarking Benchmarking is a process by which organizations evaluate their performance in comparison to their competition or to best practices found internally or in outside organizations. It was pioneered by Xerox in the late 1970s in response to growing pressure in the photocopy industry. Benchmarking is now recognized as an important input to strategic planning. It can be applied to any business process or function, such as optimizing inventory levels or improving service delivery. Benchmarking can help an organization identify new ideas and methods to improve operational effectiveness. It can help break through institutional barriers and resistance to change because some other organization has already demonstrated that the new methods are more effective. Once these best practices are identified, the organization can develop plans to adopt them in their own organization. In this way, benchmarking can become an integral part of the continuous improvement process. Internal benchmarking is used to compare performance between plants or divisions. Competitive benchmarking is used to assess performance relative to direct competitors within an industry. Internal and competitive benchmarks are useful in identifying gaps in performance. For example, automotive manufacturers use customer surveys to compare quality and customer satisfaction. Poor performance must be addressed to ensure survival in the marketplace. However, competitive benchmarking may not identify the best practices needed to close the gap in performance. Furthermore, although benchmarking internally or among competitors may identify incremental improvement opportunities, it is not likely to identify breakthroughs leading to world-class performance. Collaborative benchmarking requires cooperation between two or more organizations. Each organization freely shares information about their best practices in exchange for information about other best practices from a partner. Suppose, for example, Wal-Mart wishes to team with Dell Corporation. Wal-Mart offers to share information on forecasting consumer demand, and Dell reciprocates by sharing insights on how they minimize order-to-delivery times. With collaborative benchmarking, the key is to identify the very best performer. Use trade associations, publications, financial analysis, market research, or other tools to find the leader. External benchmarking may identify the best opportunities, but it requires a significant investment of time and effort. It may be useful to employ internal benchmarking first because it will generate quicker results. Internal successes should receive recognition. This will convince skeptics that the process works. The benchmarking team also will gain valuable experience and be better prepared for pursuing external benchmarking partners. A typical benchmarking project will include:



17

• Data collection. Develop a mutually acceptable protocol with the partner, including a code of conduct, confidentiality agreements, and performance measures to be analyzed. Data sharing may include information about procedures, standards, software, training, and other supporting systems. The key is to gain enough understanding and direction to replicate the best practice within your organization.

Part I.B.2

• Planning. Identify what is to be benchmarked. Establish the objectives for the study. If the scope is too narrow, the benefits will be limited. If the scope is too broad, the task may become unmanageable and the probability of successfully implementing the best practices will diminish. Select the team members and search for target organizations to benchmark.

• Analysis. Assess the data for accuracy and credibility. Determine current performance levels and identify gaps. Explore the feasibility of implementing the best practice. Some practices are not readily transferable— is adaptation necessary? Forecast the expected improvement. • Implementation. Obtain the support of key stakeholders. Use project management techniques or action plans to initiate the change. Monitor performance. Document activities and communicate progress. Benchmarking is not a precise discipline, and common pitfalls include lack of commitment, insufficient planning, comparing processes that are not sufficiently similar to generate useful insights, and measuring processes that have little potential for significant gains. A well-executed benchmarking project will help both in deploying strategic plans and suggesting modifications to future strategic plans. But real leadership means not just catching up with industry leaders, but surpassing them. Benchmarking can never accomplish that.

Stakeholder Identification and Analysis Congruence between policy and results is evaluated through audits that periodically check for conformance. The stakeholders need to be clearly identified and their differing needs must be met. If adaptation of a policy must occur, it must remain within the original intent if the policy is to remain credible to the stakeholders. Frequent feedback from all stakeholders helps to quickly identify and correct any disparity. Performance measures, discussed below, must take into account the differing needs and perceptions of each stakeholder group. Stakeholders include the following: • Stockholders, the owners of the company. Their role is often passive and their needs are primarily of a financial nature. They expect the company to maintain its credibility in the financial markets and hope for growth in earnings and share price. • The executive group, including the board of directors and the top tier of managers. They must acknowledge and serve the other stakeholders. Conversely, the health of any organization is critically dependent on their decision making and deployment.



Part I.B.2

18


• Employees other than top management. This critical group of stakeholders has little direct impact on policy but all other groups depend on them to carry out the policy efficiently and promptly. The quality of any organization’s end product depends on how well the employees are recruited, trained, and supervised. • Suppliers and customers. These two groups are concerned with external inputs and outputs. Suppliers must adhere to contractual requirements and therefore can insist on fair and prompt payment for their goods and services. Customers are paramount stakeholders, for if customers do not want the organization’s products, it will eventually cease to exist. Two later chapters deal with customer relations and supplier management. • The community at large. Communities, neighbors, environmental regulators, law enforcement agencies, chambers of commerce, legislatures, and similar bodies often are indirect stakeholders. Individually their impact is relatively slight but if a major issue arises, the concerns of a community can have an overwhelming influence. This stakeholder group is especially critical when plants are being planned for opening or closing. The community often is concerned about treatment of minorities, public service (or the absence thereof), and environmental abuses.

Performance Measurement Tools The strategic plan is a vision with broad goals and objectives for the organization to achieve. Management at all levels is charged with implementing the strategic plan. Metrics must be developed to monitor activities and track progress toward achieving the goals and objectives. But before discussing numbers and types of metrics, it is important to emphasize that the metrics should reflect the strategic vision. Some authors use the word linkage to describe the connection between strategic goals and performance metrics. We are on the right path if people two or three levels down from top management in the organization can articulate how their activities support a strategic objective. Once the strategic plan is finalized, management must cascade the goals and objectives down through the organization and identify specific tasks with time lines, methods, and responsibilities. This is not a trivial task. Considerable care should be taken to select appropriate measures. Stakeholders and subject matter experts within the organization should be involved in the selection process. Team participation is more likely to result in performance measures that are aligned with strategic objectives. Participation also fosters ownership of the metrics. Some managers go a step further and link the objectives to annual employee performance evaluation programs or to bonus programs. For a clear example of how to cascade performance measures, we can look to the field of reliability engineering. When designing a system, we establish reliability targets for the system as a whole. When designing the components of the system, we must establish more stringent reliability targets for each component so that the system as a whole continues to meet the overall performance target. This process, called reliability allocation, is a highly technical process that should be performed



19

Part I.B.2

by someone with expertise in reliability. Unfortunately, management science has not progressed to the same level of discipline as the reliability field. Nevertheless, the basic concepts still apply. When cascading a high-level objective down to operations, we must allocate tasks and apportion the targets to ensure that the organization as a whole will meet the objectives. For more details on reliability concepts see Chapter 20.

Guidelines for Performance Measures • Measures should be linked to strategic objectives. • Measures should be rigorous, objective, quantifiable, and standardized. • Measures should be achievable, realistic, and time-based. • Measures should be assigned to appropriate personnel who are held accountable and who are empowered with some level of control to influence outcomes. • Focus on the vital few. Many authors suggest using no more than two dozen measures. Use your judgment. Avoid using too many metrics, which may dilute the results. • Automate data collection and calculations if possible. Spend more time making decisions than generating reports. • Select measures that are resistant to perverse behavior. Most of these guidelines are self-evident but the last bullet warrants explanation. Suppose an organization faces stiff competition in a commodity market. Cost reduction is a key strategic initiative. When the objective is cascaded to plant operations, the maintenance department decides to support the objective by postponing costly equipment overhauls. This “perverse” behavior may help in the short run but could cause a catastrophe in the future. How can this be avoided? One solution is to use combined metrics. For example, we could create a maintenance productivity metric: Maintenance productivity =

∆R MTTF

∆R Maintenanc e budget * ∆R MTTR

In this metric, bigger is better. The symbol ∆R is applied to each variable and refers to the ratio of the variable in period t divided by the variable in period t–1. This little math trick results in a dimensionless equation that is “normalized” to a value of 1.0 when there is no change in the variable from one period to the next. If the productivity value is greater than 1, performance is improving; if less than 1, performance is deteriorating. Since maintenance spending is in the denominator, less spending is encouraged because it will increase the productivity metric. But we can also increase the productivity metric by increasing the equipment mean time to failure (MTTF) or by decreasing the mean time to repair (MTTR) (for more



Part I.B.2

20


details see Chapter 20). If the maintenance department starts scrimping on the budget, breakdowns will probably occur more frequently and repair times may increase. Declining performance will offset the benefit of reduced spending in the metric. Thus this combined metric encourages appropriate behavior. The point of this example is not to advocate specifically for a maintenance productivity metric but to suggest that a little creativity can overcome inherent weaknesses in traditional performance measures.

Balanced Scorecard Robert Kaplan and David Norton introduced the balanced scorecard in 1992. Refer to the list of stakeholders above. Kaplan and Norton argued that most strategic plans were unbalanced because one stakeholder group—the stockholders—was overemphasized. They proposed a “balanced” scorecard with four perspectives: 1. Financial fundamentals 2. Business processes 3. Customer 4. Learning and growth Financial measures include traditional indicators such as cash flow, sales, and return on investment. Business processes include manufacturing measures such as yield and rework. It can also include support activities such as order processing. Customer measures may include trends in customer satisfaction or average wait times on telephone hot lines. The learning and growth perspective recognizes the human element in an organization and looks at softer measures such as participation in employee suggestion programs and training. The balanced scorecard provides a framework to translate the strategic plan into specific tasks that can be managed by frontline employees. In a typical scorecard, the objective is listed along with associated measures, targets for performance, and initiatives that will drive the organization to achieve the objective.

Dashboard A dashboard provides a visual, at-a-glance display of key business indicators (see Figure 2.3). Dashboards provide a compact view of the current organizational state. Dashboards may include trend charts, bar charts, and green/yellow/red lights to indicate performance relative to target. Some dashboards include “drill down” features so that managers can dig into lower-level data. Digital dashboards must be customized for various activities throughout the organization. High-level dashboards are appropriate for executives, but frontline employees need to access low-level data appropriate for their sphere of influence. The elements in a dashboard should be linked to the strategic objectives. Sales are targeted to grow at 3.75 percent per year. To avoid revealing confidential information, the dashboard shows only deviation to target. Sales below target are negative. In Figure 2.3, although sales in the recent past have fallen short of the goal, the trend is favorable. Inventory turns (annual sales divided by current inventory)



Chapter 2: B. The Quality Management System

Deviation to quarterly sales target ($ 1000s)

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Part I.B.2

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have met or exceeded the target in two of the past three quarters. The milestone review for new product development shows two tasks behind schedule. The yearto-date (YTD) performance to target chart includes several elements that were selected in the balanced scorecard process. Calculating the ratio between actual performance and the target allows us to combine various metrics on a single chart with a common scale. In this example, management should be concerned that employee suggestions are not being closed promptly and customer calls are still not being processed fast enough through the call center. Remember that what you measure will determine to a great extent the activities of your organization. Therefore, carefully select the metrics for your dashboards, scorecards, and other performance measurement tools. More information on performance measures such as process capability indices is presented in Chapter 38.

Planning and Scheduling Follow the old adage: plan your work, work your plan. Work planning requires a clear understanding of the overall goal and the objectives—also referred to as outcomes—to be achieved (the “what”). The planning process must also take into account how the initiative relates to other projects (for example, sharing of



Part I.B.2

22


resources), and therefore often requires input from or participation by multiple stakeholders. Figure 2.4 shows an action plan format that can be used to document the plan, while Figure 2.5 shows a format for an implementation schedule. A Gantt chart may be added to show the timing of each step in the schedule. Periodic work review meetings are held to provide: • A prescheduled meeting between the project leader and the manager to discuss progress of the project • A summary of performance (presuming day-to-day feedback was given), evaluation of progress, determination of actions to correct/improve performance, and renegotiation of such activities as may be necessary • A meeting where both parties come prepared with documentation relative to the work objectives • An effective time for the manager to reinforce work done well, assuming the work climate is conducive to frank, open, two-way discussion and problem solving The specific time to review progress is a matter of preference. Different objectives or projects may be reviewed at different time intervals depending on complexity, time span of work, competency level of performer, criticality of work outcomes, disruptions in due dates, resource shortages, and so on. As a rule of thumb, work reviews should be scheduled at least once a month for objectives spanning more than a three-month period. It is never appropriate to wait until just before the planned achievement date to review progress on work objectives.

Project Management Quality engineers often become involved in project activity—either as a project team member or as a project leader. A number of proven techniques and tools are available to assist in cost-effective project management. The first is proper project selection. Project Justification and Prioritization Tools. Projects must be prioritized to select those having the most merit. Projects should be evaluated for their fit to overall business needs, financial payoff, and potential risks. Exceptions will be made for legal mandates and customer demands. Only projects that are optional should be prioritized. Major projects involve risk of loss. Risk assessment involves identifying potential problems that could occur, their impact, and what, if any, actions should be taken to offset them, such as taking countermeasures, purchasing risk insurance, or developing contingency plans. For complex projects, it may be prudent to apply a formal risk assessment tool such as a failure mode and effects analysis (FMEA) or simulation. (See Chapter 20 for more details on FMEA.) If the benefits of a project are uncertain and multiple outcomes are possible, then a decision tree can help to estimate the expected value of gain or loss. A decision tree lists the potential outcomes and assigns a probability to each branch. The



23

Action plan Plan no.:

Description

Date initiated: Date needed: Approval: Team (L): Team (M):

Part I.B.2

Objective/plan title:

Team (M): Team (M): Team (M): Major outcomes desired/required:

Scope (Where will the solution/implementation be applied? What limitations exist?):

By what criteria/measures will completion and success of project be measured?

Assumptions made that may impact project (resources, circumstances outside the project):

Describe the overall approach to be taken:

When should the project be started in order to meet the date needed/wanted?:

Estimate the resources required (time and money):

Outline the tentative major steps to be taken, a projected start and complete date for each step, and the person to be responsible for each step. (Use the back of this sheet to sketch your time line.)

Figure 2.4

Action plan format example.

© 2000 R. T. Westcott & Associates (Reprinted with permission of R. T. Westcott & Associates)




Action plan implementation schedule Step no.

Depends on step

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Finish date

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Part I.B.2

Activity/event description

Figure 2.5

Action plan implementation schedule example.

© 2000 R. T. Westcott & Associates (Reprinted with permission of R. T. Westcott & Associates)

financial payout for each outcome is shown at the end of the branch. A few simple rules apply to the creation of a decision tree: • At each branch point, the probabilities must sum to 1.0. • The expected value for each branch is calculated by multiplying all the probabilities along the branch by the financial payout. • Add the expected payout for all the outcomes within a decision branch. • Choose the decision with the highest payout. Additional Project Justification Tools. There are many other financial methods for justifying projects. Three very common methods of justifying projects are: • Payback period. The number of years it will take to recover the investment from net cash flows. • Net present value (NPV). Taking into account the time value of money, NPV involves finding the present value of each cash flow (yearly) discounted at the cost of capital percentage used by the organization, summing the discounted cash flows, and determining if the project is a candidate for approval based on how positive the NPV is. • Internal rate of return (IRR). A discount rate that causes the NPV to equal zero. If the IRR is greater than the minimum required by the organization for typical capital investments, the project is a candidate for acceptance.



25

EXAMPLE 2.1: DECISION TREE EXAMPLE

Reduce variation? Choice

Financial payout

Yes [0.85]

$150,000

No [0.15]

$ 50,000

Yes [0.85]

$100,000

No [0.15]

$0

Yes [0.8]

New machine $110,000

Increase capacity?

Part I.B.2

A quality engineer is considering several options to fix a problem with a production machine. The machine is starting to wear out, so it has excessive variation and approximately one percent of production must be scrapped. He can replace the machine with a prototype machine. There is an 80 percent chance the new machine will eliminate the variability problem and it will probably increase capacity by two percent. The second choice is to overhaul the machine, with a 60 percent chance of improving the yield. The third choice is to perform selected repairs. This choice has the lowest initial investment but also is least likely to solve the variability problem. This problem is summarized in the decision tree below. The probabilities associated with the choices are shown in brackets.

No [0.2] Yes [0.6]

$ 50,000

No [0.4]

$0

Yes [0.3]

$ 50,000

No [0.7]

$0

Overhaul $ 35,000

Selective repair $ 15,000

Currently, the variation problem generates scrap worth $50,000 per year. A two percent increase in capacity would be worth an additional $100,000 profit per year. Therefore, the financial payout changes depending on whether the scrap is eliminated and the capacity is increased. The expected value for a decision is given by the equation: EV = Σx p(x) where x is the financial payout, and p(x) is the associated probability of the outcome. We sum all the values within the decision branch. Therefore, the expected value of the new machine is: EV = (0.8)(0.85)$150,000 + (0.8)(0.15)$50,000 + (0.2)(0.85)$100,000 + (0.2)(0.15)$0 EV = $125,000 Note that the expected value of the new machine is less than the maximum payout because there is a chance the new machine will not work perfectly. We can calculate the expected value for the other options using the same approach. Continued




Part I.B.2

Continued

For the overhaul: EV = (0.6)$50,000 + (0.4)$0 = $30,000 For the repairs: EV = (0.3)$50,000 + (0.7)$0 = $15,000 Finally, we must subtract the initial investment from the expected value to get the net return. New machine = $125,000 – $110,000 = $15,000 Overhaul machine = $ 30,000 – $ 35,000 = ($ 5,000) Selective repairs = $ 15,000 – $ 15,000 = $ 0 In the first year, we will make money on the new machine, we will break even using repairs, but we will lose money if we select the overhaul. (Note: when evaluating projects, you should always consider the savings in future years, not just the first year.) At the end of the first year, we will gain experience with the option that we implemented. We can update the probability assumptions and repeat the decision tree exercise in subsequent years.

The payback period is widely used because it is so easy to calculate and simple to understand. In the decision tree example above, the payback period for installing a new machine was less than one year, which implies a very high return on the investment. But a major weakness of payback is that it does not give any insight into the magnitude of future savings, that is, savings after the initial investment has been recovered. Internal rate of return (IRR) rectifies this deficiency, as does net present value (NPV). Both give more accurate information, provided that suitable estimates of future cash flows can be obtained. The major difference between the two methods is that IRR generates an interest rate that balances all future cash flows against the present outlay, while NPV generates a dollar amount of present and future cash flows. With both calculation methods, bigger is better. Many companies have an internal hurdle rate, such as an IRR greater than 10 percent or 20 percent, that projects must achieve to be considered. The company probably could not consistently earn such a high return on stocks or bonds, yet they require projects to clear this hurdle. One reason for this conservatism is the difficulty of getting accurate estimates of future cash flows. A final cautionary word about project estimating: sometimes things do not work out as planned. Assumptions may be misleading, probabilities may be optimistic, and factors beyond your control may come into play. If you enter the calculations in a spreadsheet, it is easy to make adjustments and perform a sensitivity analysis. For example, how much would the NPV change if the probability of success decreased by 10 percent? This is sensitivity analysis. For more details and examples see Park (2007).



27

EXAMPLE 2.2: NPV EXAMPLE

A

B

C Cash flow

Year

Cash flow New machine 0.10

0.10

Interest rate

3

0

($110,000)

($35,000)

Initial investment

4

1

$125,000

$30,000

First year, expected value

5

2

$125,000

$30,000

Second year

6

3

$110,000

$15,000

Offset savings, paying for repairs

7

4

$110,000

$15,000

$15,000 in repairs

8

5

$105,000

$10,000

Machine is starting to wear out

9

6

$ 98,000

$8,500

Variability increasing, yield decreasing

10

7

$ 62,000

$2,400

Time to replace machine?

1

2

Overhaul

Part I.B.2

The NPV method converts all future cash flows to today’s dollars at a specified interest rate. It is easy to calculate using a spreadsheet. From the decision tree example above, we enter the initial investment and the expected values of the payouts for year 1, year 2, and so on. In year 3, the warranty expires and we start performing repairs. After year 5, the machine is starting to wear out, and by year 7, we are ready to overhaul or replace the machine. Note: the NPV example shown here can be understood without reference to the decision tree above. D

Comments

To calculate the NPV for the new machine using Excel software, and with the data entered in the spreadsheet as shown in the table above, click in an empty cell and type: =NPV(B2,B3:B10) The first cell reference inside the parentheses points to the interest rate. The interest rate should be the prevailing rate for raising cash in capital markets (that is, a bank loan). Ten percent is typical. Enter this as a decimal in the spreadsheet. The second cell reference refers to a range and shows where all the cash flows are, including the initial investment. The NPV function assumes that the initial investment is made at year 0 and the first payout is at the end of year 1. Column A is shown for reader comprehension, but it is not needed by Excel. The results are surprising: NPV, New machine = $379,136 NPV, Overhaul = $ 46,200 The net return for the new machine option in the first year was $15,000. But when you consider the life of the investment, the return is enormous. The overhaul option loses money in the first year but proves to generate positive cash flows in subsequent years. The selective repair option has zero NPV—it is a basic maintenance strategy.




Part I.B.2

Table 2.1

A typical project planning sequence.

S#

Tool/Technique

Comment

01

Statement

This is where the kernel of an idea or the basic concept visualized is translated into a clear statement of the problem, deficiency, or opportunity to be realized. Careful definition at this point helps later to clarify the scope of the project.

02

Project justification

Risk analyses and assessment (payback period, NPV, IRR, ROI, ROA, and benefits/cost). Go/no-go decision made.

03

Drafts of mission statement, project scope, and project objectives

These documents clarify the overall direction of the project and what it is to accomplish, the breadth and depth of the project, and the measurable objectives by which progress and completion are to be measured.

04

Stakeholder requirements

Stakeholders would consist of two groups: (1) those with a direct commitment to the project team, for example, a process manager who provides a skilled person to serve on a process improvement team working to reduce machine downtime and (2) those without involvement but who can influence project results, for example, the purchasing department that selects the vendor for a new machine. A macro-level process map may be used to identify areas from which potential team members should be selected.

05

Project team formation

Team members should be selected based on the need to represent a stakeholder group and/or specific skill sets required. Stakeholder groups not represented on the project team should have opportunities to provide input. Some members may be required on an as-needed basis only. Whenever possible, the interests, values, and personality profiles of individuals nominated should be considered. The Myers-Briggs Type Indicator (www.myersbriggs.org) can be a useful tool for building a team with complementary interpersonal skills and interests.

06

Finalized mission statement, project scope, project objectives, and project charter

Team members refine the original drafts. A benchmarking study may be appropriate to better define target outcomes.

07

Contractual requirements and deliverables

All requirements and outputs of the project are identified, defined, and documented.

08

Work breakdown structure (WBS)

Project work is further defined by breaking the work down into a hierarchy of work categories (families of like work clusters) down to the task level. Boxes on a WBS may be annotated with “person/ work unit responsible,” “resources required,” “cost estimates,” various other cross-references, and so on. Continued



Table 2.1

29

A typical project planning sequence. (Continued)

Tool/Technique

Comment

09

Gantt chart

Major project steps or task clusters are listed vertically on a time line chart with each item’s estimated start-to-finish time depicted as a bar across the chosen time intervals (weeks, months, quarters). As the project progresses, the same chart may be used to plot the actual time expended next to the estimated time. Major milestones are shown as points along the time bar.

10

Time-dependent task diagram (AND, CPM, PERT charts)

Depending on the size, complexity, and duration of the project, it may be necessary to plot the time dependencies of each task to each other task. An activity network diagram (AND) depicts the interrelationships of each task, or task cluster, in the project. A critical path method (CPM) chart adds the dimension of normal (most likely) time to complete tasks and allows for computing the critical path (longest time line) through the project. A program evaluation and review technique (PERT) chart adds two additional time estimates for each task (optimistic, pessimistic), allowing further “what if” planning. Typically AND is used for shorter-term, simpler projects, CPM is used where there is data available for reasonably accurate time estimates, and PERT is most often used for projects for which there may be no prior precedent.

11

Resource requirements matrix (RRM)

An RRM delineates the various types of resources needed (for example, personnel, facilities, equipment, materials, consultants, and so on), quantity, when needed, and cost.

12

Linear responsibility matrix (LRM)

An LRM, for larger projects, defines the interfaces: who has what responsibility for what tasks, and to what degree (for example, primary, secondary, resource only, need to know).

13

Project budget

A detailed, itemized budget is prepared based on the time and cost estimate prepared by the team.

14

Measurements

The quantifiable measurements by which project progress and determination that project objectives have been achieved are defined. The progress monitoring process, methods for analyzing data gathered, reporting protocols, and checkpoints for initiating corrective action are determined and documented.

15

Approved project plan

Final approval of the project and authorization for implementation is given.

Part I.B.2

S#

Project Planning and Estimation. Success of a project is significantly impacted by effectiveness of project planning. A typical project-planning sequence for a larger project is outlined in Table 2.1. Examples of some planning documents are provided in the following figures:



Part I.B.2

30


• Figure 2.6 is a three-level work breakdown structure (WBS) under development. A WBS allows determination of the many activities that must occur during the project. The numbering scheme in Figure 2.6 may seem unduly complex at first. But the consistent use of multiple decimal points allows nesting of levels and facilitates changes to dynamic projects. • Figure 2.7 is a Gantt, or milestone, chart for an ISO 9001 implementation showing the major implementation phases and their relative timing. The Gantt chart is one of the earliest planning tools, dating back to the early years of the 20th century. Solid bars indicate activities that require an elapsed period of time, while triangles denote events that occur at specific points in time. The figure is fairly primitive; computerized Gantt charts can involve multiple layers and interactions of activities.

1.0 1.1

1.2

1.3

1.4

Figure 2.6

ISO 9001 Quality management system implementation project Quality system documentation 1.1.1 Quality policy and objectives 1.1.2 Quality system manual (QSM) 1.1.3 Quality system procedures (QSP) 1.1.4 Quality system work instructions (WI) Training 1.2.1 ISO 9001 briefing 1.2.2 Steering committee meetings 1.2.3 Management representative training 1.2.4 Internal auditor training 1.2.5 Audit behavior training 1.2.6 Statistical process control training Quality system implementation 1.3.1 Calibration system 1.3.2 QSPs and WIs 1.3.3 Supplier qualification process 1.3.4 Document control system 1.3.5 Auditing schedule 1.3.6 Customer information system 1.3.7 Corrective/preventive action process Controls 1.4.1 Document control 1.4.1.1 QSM, QSP, WI 1.4.1.2 Forms 1.4.1.3 External documents 1.4.2 Audits 1.4.2.1 Internal audits 1.4.2.2 Preassessment 1.4.2.3 Certification assessment 1.4.2.4 Surveillance audits 1.4.3 Corrective/preventive actions 1.4.4 Supplier evaluations 1.4.5 Management reviews

Work breakdown structure (partial).


Figure 2.7

Gantt chart example.

Registration—celebrate

Conduct final assessment

Take corrective action

Conduct preassessment

Conduct audit behavior meeting

Prepare quality system manual

Conduct internal audits

Select, schedule registrar

Implement QSPs

Train internal auditors

Evaluate registrars

Employee kickoff meeting

Prepare work instructions

Prepare quality policy, objectives

Prepare quality system procedures (QSP)

Form steering committee

Conduct gap analysis

Conduct ISO 9000 briefing

Select consultant

Task Weeks 1–13

Weeks 14–26

Weeks 27–39

Weeks 40–52

Weeks 53–65

Weeks 66–78

Part I.B.2

18-month ISO 9001 quality management system implementation project

SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 2: B. The Quality Management System 31



Part I.B.2

32


• Figure 2.2 depicts a critical path method (CPM) chart showing every activity in the project and how its start depends on the completion of other activities. The sequence that takes the longest total time constitutes the critical path and determines the minimum time to completion of the project. Resource requirements matrices (RRM) are essentially spreadsheets laying out the requirements over time against the activities in the project. RRMs may be compiled for facilities, equipment, materials, contract/consulting services, personnel, and so on. The project budget details the anticipated expenditures over time for each category of expense. Depending on the size of the project, budgets may be prepared for successive levels of the project (usually paralleling the WBS hierarchy). Understanding the project lifecycle can also help in estimating the resources required. The five stages of a project are: (1) concept, (2) planning, (3) design, (4) implementation, and (5) evaluation and closeout. Monitoring and Measuring Project Activity and Results. Critical project performance measures include timeliness, budget variance, and resource usage. Project measurements must then be determined and a system for tracking, monitoring, and reporting progress is established. In medium to large projects, milestones (critical checkpoints) are established in the planning stage and the project monitored against these milestones. The critical path method (CPM) is discussed elsewhere in this chapter and illustrated in Figure 2.2. A CPM can be built into the quality information system for projects of any size. Thorough periodic project reviews are conducted, including assessment of schedules against the critical path, expenditures against budgets, resource utilization against plans, implementation results achieved, a possible reevaluation of risks, and any major issues impacting project continuance. Based on these reviews, the project may be continued as planned, modified, put on hold, or canceled. A similar review is conducted to evaluate the results when the project is completed. Project Documentation. A project is not finished until the paperwork is completed. Documenting the project all along will make it easier to complete the paperwork that closes the project. If the team has not documented every aspect of the project, begin to document as soon as you can in order to capture details such as the following while they are still available: • Assumptions, risks, and rationale for selecting the project • Decisions made to initiate project and approvals • Detailed plans for design and implementation • Design and/or implementation changes • Major obstacles encountered and how they were resolved • Details of implementation (for example, measurements established) • Progress reports and resulting decisions



33

• Final evaluation of project results • Results of post-project audits

Part I.B.3

All documentation is valuable in planning and estimating new projects and in avoiding previous mistakes. Likewise, the documented knowledge base is a tool for training those new to project management.

3. QUALITY INFORMATION SYSTEM Identify and define the basic elements of a QIS, including who will contribute data, the kind of data to be managed, who will have access to the data, the level of flexibility for future information needs, data analysis, etc. (Remember) Body of Knowledge I.B.3

A quality information system (QIS) is a collection of data, rules, and equipment that creates information about quality in a systematic way. A QIS will collect, store, analyze, and manage quality-related data from customers, suppliers, and internal processes. It will generate information in the form of printed reports, screen displays, and signals sent to mechanical devices. Depending on the degree of automation, it may give answers to questions posed by humans, or it may have built-in action rules. Above all, if it is well done it will enhance profit and productivity.

Concept and Objectives The first requirement in studying quality information systems is to understand what, exactly, a “system” is. The word is used in many different contexts. For example, this book discusses management systems, information systems, strategic planning systems, and quality systems, just for starters. From other sources you can learn about transportation systems, manufacturing systems, educational systems, social systems, gambling systems, and planetary systems (for example, the solar system). The essence of a system is this: it ties a number of components together that act in common with each other. Systems that quality engineers are interested in are dynamic and goal-oriented. They have inputs, outputs, operating rules (procedures or transformational processes), data storage, and boundaries. They are designed by people to achieve specified goals. The term “system” does not imply “computer,” but in today’s computerintensive world the term “information system” generally evokes computerized information systems. There are large-scale, well-run manual information systems, and there are computerized systems that are recognized as abominations. The manual systems often have evolved over a long period of time through informal



Part I.B.3

34


cooperation. Computerized information systems are explicitly designed, and usually by cross-functional teams. A quality information system is both a quality system and an information system. It is naïve to speak of “the” quality information system, because an effective organization will have numerous quality systems, which may be manual, computerized, or hybrid (with both manual and computer elements). A well designed information system allows information generated at one level or in one part of the organization to be used for many different purposes.

Uses of Quality Information Systems Information systems may be used to: • Initiate action (for example, generating a shop order from a customer’s order) • Control a process (for example, controlling the operation of a laser cutting machine within given specification limits) • Monitor a process (for example, real-time production machine interface with control charting) • Record critical data (for example, measurement tool calibration) • Create and deploy operating procedures (for example, an ISO 9001–based quality management system) • Manage a knowledge base (for example, capturing, storing, and retrieving needed knowledge) • Schedule resource usage (for example, personnel assignments) • Archive data (for example, customer order fulfillment) The importance of information systems becomes apparent when looking at their impact on various aspects of quality management. Both process management and problem solving require accurate and timely information. Contrast the following two cases: One information system might be hard-wired into manufacturing and testing equipment, with monitors displaying real-time information complete with alarms and action signals; it could have options for graphic display of statistical and trend analysis for quick intervention. Another system in the same plant could tie executives, project teams, and off-site employees together through an intranet; organizational objectives and milestones appropriate for each level and function could be displayed as both text and graphics, along with actual performance and gaps. These two QISs are quite different. Good information systems are critical to cross-functional collaboration, since distributed information access is required in order for groups and employees to make quicker and better decisions. For example, some projects can be carried out largely through computerized discussions and transmission of documents. Often this enables highly skilled team members to participate regardless of their physical location and can also reduce the amount of time required for the project.



35

Part I.B.3

The modern quality engineer must be competent in the selection, application, and use of hardware and software technology appropriate to the tasks and responsibilities assigned. Consideration should be given not only to the functionality of the system for the task, but also issues such as required user skills, compatibility with other systems, and information security. Furthermore, if the quality system is of any magnitude, the quality engineer must understand project management techniques and must be a good team member.

PLC and SCADA Systems The widespread use of microcomputers and programmable logic controllers (PLCs) has transformed the factory floor. There is a growing trend toward distributed measurement and control, where PLCs have built-in programs and logic to control machines and processes. Fewer and fewer technicians are turning dials or opening and closing valves to control processes. These tasks are now controlled by PLCs. But many PLCs do not have a human interface such as a monitor or keyboard. The PLCs are widely distributed throughout the plant, so manual data collection is time-consuming and cumbersome. Furthermore, PLC language is not user-friendly. This situation has given rise to large-scale supervisory control and data acquisition (SCADA) systems. The SCADA system interfaces with all of the PLCs through a network. The SCADA system periodically polls the PLC memory registers to collect data. The system includes a human interface, usually in a central location such as a control room, to monitor the processes, generate alarms, and allow the operator to intervene or override as necessary. The SCADA system typically includes real-time trend charts and graphic displays of the current status of the equipment. The system also provides for data storage in a database program, which provides rapid retrieval of data for subsequent analysis and reporting. What is the role of a quality engineer in the creation of a large-scale SCADA system? The information system should be viewed as no different from a manufacturing system. The QE should be involved from the earliest planning stages to ensure that user and system requirements are thoroughly documented. It may be appropriate and beneficial to apply some of the advanced quality planning disciplines discussed in Chapter 17, even though the “product” is a software system. For example, customer requirements should be fully understood, even if the “customer” is an hourly employee who will use the system to monitor and adjust the process. The quality engineer should participate in creating the user requirements—after all, the quality engineer is typically considered the local expert in data analysis and reporting. What reports are needed? How should the data be displayed and summarized?

Information System Strategy and Tactics Although there are many ways to design information systems, it is a truism that the larger they get the more fraught with risk of failure they become. So the quality engineer can render a real service to the employer by studying strategy and tactics of systems development.




Part I.B.3

What

Who

Where

Hardware

List of physical components of the system

Individuals who use it, individuals who manage it

Physical location

Software

List of programs, applications, and utilities

Individuals who use it, individuals who manage it

What hardware it resides on and where that hardware is located

Networking

Diagram of how hardware and software components are connected

Individuals who use it, who manage it, and the company from whom service is obtained

Where the nodes are located, where the wires and other transport media are located

Data

Bits of information stored in the system

Individuals who own it, individuals who manage it

Where the information resides

Figure 2.8

Information systems strategy matrix.

The need for a strategy was emphasized by Pearlson and Saunders (2004) who produced an information systems strategy matrix, as shown in Figure 2.8. In this matrix, four different categories are displayed: hardware, software, networking, and data. Other categorizations could be made. This is just a small example of the kinds of analysis required. Another tool to consider is the V-model.

The V Model The V model starts on the left side at the top of the V (see Figure 2.9), with highlevel user requirements, and cascades down through functional specifications and detailed design requirements. On the right side of the V, test protocols are developed, executed, and documented to verify that the design specifications have been met. The QE should be involved in this process to ensure quality and data integrity during the execution of the project. Tasks that seem trivial, such as naming conventions, can have a huge impact down the road. Large real-time control systems may have hundreds of PLCs and thousands of sensors. Imagine the complexity of creating a downtime report for the packaging area of the plant. Every machine and sensor in the area must be included in the database query. A good naming convention will allow a group of variables to be captured with a single query statement that includes a “wild card.” If a naming standard is not used or is poorly executed, then the user has no choice but to individually specify each sensor and PLC when the database query is created. Similar care and consideration should be given when creating the test protocols. How much data should be collected? How often will the samples be collected? If the sampling duration is too short, or the elapsed time between samples is too long, then it may not be possible to detect variation that is directly caused by the PLC control system. Is there a difference between the process target and the actual steady-state process average? What about including process upsets in the test protocol? Does the controller overshoot the target during initial recovery?



User requirements specification

37

System test protocol and user acceptance

Verify

Detailed design specifications

Part I.B.3

Hardware test protocol

Functional specifications

Software module test protocol

System development

Figure 2.9

The V model for software development.

Further ideas to improve the success of system development projects are reported by Long and Gryna (1999), who drew the following conclusions: • Carefully define the scope of the QIS and what it is expected to accomplish. From the very beginning emphasize operational benefits, not technical specifications. It may be wise to develop a pilot project that can be used to show what really does work and what does not. Getting some benefits in a short period of time builds confidence, not only in the system itself but in the competence of the system developers. • Be sure that the goal of the QIS supports the goal of the business. (This point was discussed earlier in this chapter when we discussed strategic planning.) Once the goal is set, use well-proven project management techniques. • Get advance agreement on who will do what and when. Get buy-in to clearly understood milestones. Do not simply delegate the project to the information technology (IT) folks but keep quality engineers and managers fully engaged in the development. • Concentrate on user expectations and how they are being realized. Focus attention on the overall performance of the system rather than specific metrics. Ongoing discussion and comparison between the users and the developers is an important key to success. • Publish regular progress reports and keep the language in user terms. A common trap in large-scale information systems projects is to get bogged down in technical metrics and jargon; the user may cross their fingers and hope for the best without really understanding what is going on. A corollary of this is to be sure that the end user has the technical competency to understand what is being said. Reports cannot be watered down simply to avoid confusing the uneducated.



Part I.B.3

38


Repeatedly stress the anticipated benefits that were specified at the outset and do not abandon original goals under pressure. The exception is if it becomes clearly evident that the original specifications can not be met. Then the top-level sponsors must be fully briefed and must participate in the revised benefit statement. This should be viewed as a last resort and is in a sense a salvage operation. Productivity improvement is perhaps the most frequently cited justification to invest in an information system. The investment can be considerable because the infrastructure requires hardware, networks, sensors, customized software, and information systems support personnel. Estimating the payback can be a challenge. The payback estimates may include optimistic forecasts and tenuous assumptions. Some people focus on the human benefits such as automating periodic reports. Relief from mundane tasks will free up personnel to pursue other important tasks. But much larger gains usually can be achieved by using the information system to improve production processes. A well-designed information system can identify opportunities that probably would be missed by even the most conscientious and determined analyst using a manual or paper-based data system. At many facilities, a one percent gain in production yield is a realistic assumption and will generate a much larger return than a few hours saved per month generating manual reports.

Example of an Internally Developed QIS To illustrate the tremendous value of a quality information system, consider this case study. A highly automated packaging plant in Texas started production in 2001. Equipment breakdowns plagued the facility for the first year of production. Downtime was so excessive that the plant was operating below the break-even point. Management decided to make a major investment in a new information system. Over the course of the next year, nearly every machine in the facility was linked through a network to a database. Sensors were added to monitor key production processes. Automatic feedback systems were installed and gradually tuned to achieve stability in the most complex processes. Customized reports were created to distill vast amounts of data into usable information. The reports summarized and prioritized the current status so that management could quickly allocate resources where they were most needed. One such report is shown in Figure 2.10. The report executes automatically at the end of each production shift. It analyzes data from nearly 700 machines, identifies the top three concerns in each functional area, and prints a one-page summary. The quality department and the maintenance department worked together to develop the format. The general manager participated in establishing the equipment performance standards needed to support the balanced scorecard objectives. If performance does not meet the objectives, then the report highlights the total with a large, bold font. Management and maintenance employees can quickly identify concerns and focus their process improvement efforts accordingly. The team designing this system took several months and gave a great deal of thought to balancing the automatic collection and processing of data with the human interpretation of information. It would have been easier to design a



39

Equipment Exception Report 7/2/06 3:00 PM to 7/2/06 10:00 PM

Critical / misc machine alarms No. 2 compressor, low oil pressure No. 2 compressor, oil temperature Cooling hood jams Prior shift 14 7 3

Shop 1: Shop 3: Shop 2:

4 3 1 23 This shift 19 2 2

Number of faults

Concern Annealing oven faults

1

Shop 1, zone 5, high temperature

Coating sprayer alarms Shop 1: Shop 3: Shop 2:

1

260

No spray alarm No spray alarm No spray alarm

157 89 14

Inspection conveyor jams Shop 3, loop A Check detector Shop 2, loop C Leak test Shop 3, loop A Leak test

34 23 4 3

Discharge conveyor jams Shop 2, loop A Check detector Shop 3, loop A Leak test Shop 3, loop A Scanner

11 6 2 1

Carton forming faults CF 2 Case not at madrel CF 2 In flight jam

12 11 1

Carton packer faults Shop 1, north packer Missing jars Shop 1, north packer Elevator jam Shop 2, west packer No glue

18 13 2 1

Throughput % of budget

0 94.6% 98.1% 98.4%

Downtime summary Shop 3 Shop 1 Shop 2

Figure 2.10

(minutes) Total downtime Total downtime Total downtime

285 146 83 56

Part I.B.3

Number of faults

Concern

Shop 3 Shop 2 Shop 1

Current status report example.

completely closed-loop control system but this would have precluded human intervention and thoughtful study of what the processes were saying. But at the same time, the data on which the daily and weekly reports were based was massive and it was essential that it be condensed and summarized before being presented to humans. The plant achieved a dramatic improvement in throughput in less than six months after implementing this QIS. The report shown in Figure 2.10 (and others like it) helped drive a transformation in quality and productivity. By the end of the second year, the plant achieved best-in-class quality and their profit margin was over 10 percent, exceeding the original performance target.




Part I.B.3

Example of a Web-Based QIS Cequent Performance Products, a small manufacturing company in Tekonsha, Michigan, supplies parts to the automotive industry. In 2002 this company decided to use information systems development to automate their quality recording, analysis, and reporting. They also sought ISO 9000:2000 registration (see Chapter 12). Cequent did not want to develop their own software, so they contracted with IQS, a quality information systems vendor in Cleveland, Ohio, to provide the software on demand through the Internet. Cequent partnered with suppliers of automated test equipment (ATE) to feed process parameters, process data, and test data directly into the IQS software, which eliminated duplicate inspections. Inspectors began use roving laptop computers with wireless I/O and power supplied by car batteries on small carts. The system integrated several different small stand-alone quality systems and manufacturing resource planning (MRP) systems into one integrated factorywide system. Most of the system development was done through interactive online messaging. Using the vendor’s experience with ISO 9000 processes, Cequent was able to achieve registration within about nine months of their initial commitment to seek it. And because they followed carefully planned system development procedures, their actual operations were simplified and enhanced as a result.

Summary The two examples above are but a small sample of the tremendous number and variety of QISs now being implemented. There are so many new developments. Bar codes, voice entry, optical character recognition, local area and wide area networks, are among the host of new technologies available for cost-effective automation of quality systems. Knowledge management, audiovisual presentations, individual learning programs, decision support systems, computerized conferencing, systems modeling, automated online reference services—the list goes on and on. The foresighted quality engineer will study computerized information systems techniques and possibilities with great zeal. This is an area that will continue to revolutionize all aspects of life, both organizational and personal.



Chapter 3 Part I.C

C. ASQ Code of Ethics for Professional Conduct

Determine appropriate behavior in situations requiring ethical decisions. (Evaluate) Body of Knowledge I.C

All professions are bound by specific codes of ethics, and one mark of any profession is publishing and upholding standards of conduct. The American Society for Quality has adopted the following code of ethics:

Code of Ethics Fundamental Principles ASQ requires its members and certification holders to conduct themselves ethically by: I. Being honest and impartial in serving the public, their employers, customers, and clients II. Striving to increase the competence and prestige of the quality profession, and III. Using their knowledge and skill for the enhancement of human welfare Members and certification holders are required to observe the tenets set forth below: Relations with the Public Article 1—Hold paramount the safety, health, and welfare of the public in the performance of their professional duties. Relations with Employers and Clients Article 2—Perform services only in their areas of competence. Article 3— Continue their professional development throughout their careers and provide opportunities for the professional and ethical development of others. Article 4—Act in a professional manner in dealings with ASQ staff and each employer, customer, or client. Article 5—Act as faithful agents or trustees and avoid conflict of interest and the appearance of conflicts of interest.

Continued

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Continued

Part I.C

Relations with Peers Article 6—Build their professional reputation on the merit of their services and not compete unfairly with others. Article 7—Assure that credit for the work of others is given to those to whom it is due. ASQ’s code of ethics will help you decide how to treat your subordinates, peers, and managers, but numerous laws, as well as company policies, are applicable. Knowledge of same may be mandatory. For example, if you are interviewing someone for a position, the law requires you to follow certain rules for asking questions. Likewise, your company may have internal rules for dealing with peers, subordinates, and suppliers.

Quality engineers must be aware of legal issues, such as equal employment opportunity (EEO) laws and other guidelines. Another example of how the legal system impinges on quality engineers is the Sarbanes-Oxley legislation. Because of several instances of large-scale corporate fraud at the turn of the last century, the U.S. Congress passed this law, sometimes called Sarbox, which mandates a number of stringent requirements for corporate financial reporting that can be understood as quality assurance techniques applied to the corporate financial system. Sarbox actually enhances the role of quality engineering because it carries the same concept from the quality arena to the financial arena. Whether your work is governed by EEO, Sarbox, or other relevant statutes, the point to remember is that your personal behavior must at all times be such that no embarrassment will come to the supplier, your employer (subordinates, peers, or management), the customer, or yourself. You must be polite and diplomatic and show respect to all persons. In the final analysis, you must be honest with yourself that you have acted fairly and legally, and that you have a good feeling in your gut about the things you have been involved with, including resolving ethical dilemmas.

ETHICAL DILEMMAS Ethical dilemmas arise every day in the application of technology and its effects on human and nonhuman processes and the advancement or decline of society. Technology can harm people by inducing stress, triggering injuries, and demoralizing them. Conversely, technology can stimulate personal development and organizational growth. How technology is applied and the consequences of the application often call for ethical decisions. Some have equated the definition of quality and ethics with “do the right thing.” A case in point is the ongoing need for guidelines governing ethical behavior in the application of computers, e-commerce, e-business, and other new technologies. Some of the issues demanding critical attention are: 1. Misusing employers’ computers for personal gain or pleasure 2. Destroying others’ property (for example, injecting a virus, wiping out files, and so on)


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3. Using or condoning the use of computers for fraudulent activities

5. Omitting safeguards that protect users 6. Infringing on copyrights and trademarks

Part I.C

4. Violating individual and company rights to privacy and confidentiality

7. Failing to maintain a sufficient level of accuracy and completeness implied when data is collected and stored in computer databases 8. Failing to make critical information known to appropriate decision makers in time to prevent a negative outcome 9. Failing to capture, manage, and make available critical knowledge to those who need it 10. Failing to upgrade computer technology 11. Managing retrieval of data files from old or different software programs/versions 12. Dealing with global employees, businesses, and markets 13. Dealing with legal requirements (including safety and environmental regulations) of different governmental groups across geographic boundaries 14. Ensuring the usage quality of the new technology itself, and ensuring that people are trained to use the new technology Another area of concern to the engineer is the Occupational Safety and Health Administration (OSHA). Both federal-level agencies and state-level agencies monitor organizations to ensure compliance with the respective rules and regulations. Some of the more common sets of rules and regulations are: OSHA, Labor

(Randall’s Practical Guide to ISO 9000 provides a more comprehensive list)

29 CFR 1910.95

Occupational Noise Exposure (Ear Protection)

29 CFR 1910.120

Hazardous Waste Operations and Emergency Response

29 CFR 1910.132

Personnel Protective Equipment

29 CFR 1910.133

Eye and Face Protection

29 CFR 1910.147

The Control of Hazardous Energy (Lockout/Tagout)

29 CFR 1910.1200 Hazard Communication Engineers also are finding themselves involved with issues usually handled by management, such as interviewing potential new employees for the organization. Without the proper training, engineers could be putting themselves and their employers at great risk for lawsuits by asking inappropriate questions. Some items that the interviewers must be aware of include:




• Ask only job-related questions

Part I.C

• Do not ask about age, race, national origin, marital status, or religion • Focus on the competencies and skills for the job in question • Avoid any small talk that is not related to the job In conclusion, the ASQ code of ethics emphasizes that we are professionals and must act accordingly. Federal law and employer rules create additional requirements for compliance. You must understand all of the above, and more as it is presented to you.



Chapter 4 Part I.D

D. Leadership Principles and Techniques

Describe and apply various principles and techniques for developing and organizing teams and leading quality initiatives. (Analyze) Body of Knowledge I.D

Leadership is an essential part of any quality initiative. The leader’s role is to establish and communicate a vision and to provide the tools, knowledge, and motivation necessary for those individuals or teams who will collaborate to bring the vision to life. This can apply to an entire organization as well as each specific department or work group. For example, the leader of the quality engineering function is responsible for helping shape the policies for the quality technologies that will be deployed throughout the organization and for ensuring that department personnel are sufficiently qualified to support the use of the technologies. A leader may or may not hold an officially designated position. Often someone in a work group will emerge as a leader because of their knowledge, skills, experience, and/or abilities. Further, teams often include facilitators, another leadership role. The facilitator’s purpose is to provide support to the team’s effort, while at the same time allowing the team to maintain ownership of its decisions. A good leader always tries to understand where the other person is coming from, what makes them act the way they do—in other words, what motivates them. Good leaders recognize and apply Maslow’s hierarchy of needs. This is the assertion that people are driven by their needs and wants and that all human needs can be roughly placed in a hierarchy. Higher-level needs are not really relevant until lower-level needs are satisfied, but once a need is met, it no longer motivates behavior. The five levels are (1) physiological (hunger, thirst, sleep), (2) safety and security (protection from the elements and predators), (3) socialization, (4) ego, and (5) self-actualization. Many people never get their ego needs fully satisfied, so do not experience self-actualization needs, but all the great thinkers and leaders of the ages are in fact self-actualized. When trying to lead recalcitrant followers, it often helps to think about what need-level they are working on. Leadership of the quality engineering function involves defining and carrying out projects that support the organization’s strategic plan, as well as providing 45



Part I.D

46


the resources for and overseeing day-to-day quality engineering activities. While some of these activities may be performed by an individual, in today’s complex environment more are conducted in a team setting. Examples would include working with an advanced quality planning team to analyze repeatability and reproducibility (R&R) of a new measurement system or working with a software engineer to implement a new automated statistical process control (SPC) online package.

DEVELOPING, BUILDING, AND ORGANIZING TEAMS Since around 1980, quality concepts and team concepts have moved in tandem through the economy. Teamwork is now vital in government, space exploration, healthcare, education, and most profit-oriented businesses. The autocratic leader of one or two generations ago would be utterly perplexed by how much control has now shifted to the team level.

The Need for Teams The drive for excellence includes better deployment of people at all levels. Workers at all levels now expect to have some say in designing and implementing change, and only through change can quality improve. Managing an organization through teams has become recognized as a core component of business. There are many types and purposes of teams, each requiring different structures, skills, resources, and support. Leaders of an organization must therefore be clear about what they are trying to accomplish and ensure that the appropriate team processes are utilized for their situation. A team-based environment might be initiated as part of the strategic plan or as a response to a specific problem encountered by the organization. Regardless of the reason, there should be a process for planning and carrying out the team-based initiative. This process is often done through a steering committee that focuses on driving business improvement. A member of management called the sponsor also typically is identified and takes responsibility for initiating and guiding a team. The sponsor usually is the individual with ownership of the process or area where the team’s actions are focused.

Types of Teams Although each organization may utilize different names, three major types of teams are widely used: • Process improvement team. These are temporary teams whose missions are to develop a new process or improve an existing process. These teams are often cross-functional, consisting of representatives from multiple departments involved in the process under study. The management sponsor typically selects the team leader and will negotiate with other area managers to identify other team members appropriate for the project mission. Figure 4.1 shows how teams should be integrated within the organizational hierarchy.


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Part I.D

Facility leader

Department A leader

Department B leader

Four interlocking teams, each at a different level of the organization

Department C leader

Unit C1 leader

Unit C2 leader

Unit C3 leader

Process 1 leader

Process 2 leader

Process 3 leader

Department D leader

Unit C4 leader

Associate Associate Associate Associate Associate

Figure 4.1

Linking team structure.

• Work group. These teams consist of the personnel who work in a particular department or process area. Their mission is the ongoing monitoring and improvement of process performance and they typically meet on a regular basis (for example, weekly) to review indicators and identify any actions required. The work group leader usually is the individual with supervisory responsibility for the process area. The team also may initiate a process improvement team, especially when the improvement requires interfacing with other departments who are suppliers or customers of the work group. Organizations committed to applying work group–based improvement from top to bottom can use an interlocking team structure that includes all members of the organization. • Self-directed work team (SDWT). A self-directed work team is a group of individuals who have much broader and deeper day-to-day responsibility for management and improvement of their process area. SDWT members are highly trained in subjects such as quality, safety, maintenance, and scheduling, and in some cases also carry out human resource functions. These teams are highly empowered to make their own decisions, although of course there are still limits, such as spending authority. Whether and to what extent an organization utilizes teams usually is dependent on factors such as the rate of change in their industry, the culture of the organization,



Part I.D

48


the predominant management style, employee educational levels, and where the company’s product or service is in the maturity cycle. Some teams are less formally structured, such as an ad hoc group organized to address a customer complaint or a virtual team that wants to compare the process used for design reviews by several different facilities. Regardless, many of the following considerations will influence the success of the team and the satisfaction of its members.

Selecting Team Members The primary determination of who will participate in a team effort is whether the person is involved in the process to be improved. However, when selecting team members other issues also often are considered. For example, a process improvement team might not be very effective if all team members have the same personal style (for example, as measured by a personality evaluation instrument such as the Myers-Briggs Type Indicator/MBTI, see www.myersbriggs.org). Some teams also intentionally include someone from outside the process area who can provide a more objective, or different, view. Supplier or customer personnel also often are invited to participate when their input is deemed especially valuable. Selection of team members for organizational management and improvement is vital just as it is for a sports team. The many different activities to be carried out call for certain roles and responsibilities, which then require a certain set of skills and/or mind-set. For example, a team needs to analyze process data, minimize disruptive conflict, monitor meeting time effectiveness, and maintain records of activities. Specific roles, timekeeper and scribe, are usually defined for individuals who will carry out the latter two of these responsibilities.

Support Mechanisms Required for Team Success Team-based improvement requires more than creating teams—it requires providing them with adequate support to ensure success. Examples of support include: • Equipment. Teams need meeting space, equipment (such as tables and chairs and flipcharts), and access to computer hardware and software (for writing up meeting minutes, analyzing process data, preparing presentation materials). • Training. Unless an organization is extremely lucky, most employees who become involved in teams will not have all of the necessary skills. Such skills may include how to plan and effectively manage meetings, how to analyze processes and data, and how to make group decisions based on consensus. The organization must therefore determine the specific skills required and the current skill levels of employees, and provide opportunities to close the gap. • Management sponsor. The sponsor role is a vital leadership function that goes beyond simply launching a team. It also includes staying in contact with the team leader to ensure sufficient progress and resolving any conflicting issues with other parts of the organization. The sponsor typically has authority to cross organizational boundaries that team members would need to negotiate and can



49

therefore resolve some types of issues quicker. The sponsor also is ultimately responsible for effective implementation of the team’s recommendations.

Part I.D

• Systems change. Setting up a new team in an organization that is not adequately designed for this way of working is a prescription for failure. An organization is a system, meaning that if one part is changed, other parts will be affected. If the primary management style is autocratic and people are rewarded for competition versus cooperation, teams are unlikely to be an effective mechanism. Before beginning the team process, leadership must consider what other changes will be necessary to align the various parts of the organization. How team success will be recognized and rewarded is an especially vital component.

Team Development Each new team is a new mini-organization. The team will therefore progress— and often regress—through the traditional stages of group development that are described briefly here: • Stage 1: forming. When teams first begin to meet, each member brings his/ her individual identity and the perspective of their own environment (for example, functional process area). Even for members who have participated in other teams, each team is a unique experience and individuals often approach it cautiously, uncertain of how they will perform in the new situation. During the forming stage, a team usually clarifies its mission, specifies roles that need to be carried out and who is to perform them, and defines rules of acceptable behavior, often called norms. • Stage 2: storming. During this phase, team members finally realize the size of the task before them. They still think primarily as individuals and often attempt to shape decisions to their own advantage rather than considering the impact on other team members. Arguments, testing the leader’s authority, and attempts to change the team’s mission are typical behaviors. • Stage 3: norming. In this phase, the individuals begin to shift their focus from personal concerns to that of helping the team meet the challenge at hand. Interpersonal conflicts and the tug of external loyalties have less of an impact as team members realize their interdependence. They are more willing to discuss differences of opinion in order to understand them and how they might impact team success. • Stage 4: performing. At this stage, the team has matured to the point where it is working as a smooth cohesive unit. Team members have a good understanding of each other’s strengths and weaknesses and how they support the mission and are now able to work through group conflict. There is a greater appreciation of the importance of the team’s processes and members are more satisfied with being a member of the team. During this phase, the team typically makes significant progress toward achieving its goals. Although these stages indicate a logical sequence that occurs over time, actual progress by a particular team will vary greatly. For example, a team that has



Part I.D

50


progressed to stage 3 or 4 may fall back to stage 1 or 2 if they find that some previous assumptions about one another are not true or if team membership changes as a result of a job transfer. Some teams may not progress beyond the earlier stages due to a short project duration or if they are unable to successfully resolve group dynamics issues. Team development can be enhanced by making sure that team members have a basic understanding of how to: (1) interact in positive ways, (2) deal with difficult people or situations, (3) contribute to accomplishing the team’s goals, and (4) give or receive constructive feedback. A facilitator can help ensure that the team is aware of its progress by commenting during meetings but special interventions are also sometimes useful. Examples include simulations or outdoor adventures that allow the group members to become more familiar with each other’s styles, strengths, and weaknesses, and to become more effective at working with and through their differences.

LEADING QUALITY INITIATIVES A quality engineer is frequently called on to lead particular quality initiatives. Such projects might involve improving an existing product or service, working to resolve supplier performance issues, addressing product field performance failures, implementing new measurement technology, or obtaining ISO 9001 quality system registration. Following are some recommendations for leadership of such initiatives. Most are appropriate whether or not the project is a team-based initiative, because, by definition, most initiatives will influence others in the organization (and/or the supply chain), and the roles of others should therefore be taken into account throughout the project. • Ensure that the project mission is clear, including expected results, timing, limitations, and reporting structure and methods. Obtain supporting data used to indicate the value of the project and determine how the project is related to the bigger picture (for example, strategic plan, other projects, and/or day-to-day operations). • Determine who the other players in the project will be and make contact with them individually. Learn of their interest in and commitment to the project. • Define the technical process and the time schedule to be used to carry out the initiative. For example, a problem-solving project might use a seven-step problem-solving process, while a Six Sigma project might use the DMAIC process (see Chapter 29.) • Execute the project according to the process defined in the previous step, involving others as appropriate and keeping management informed. • Evaluate outcomes of the project against the original mission. Ensure that all people involved receive appropriate recognition for their contributions.



51

• Ensure that all involved understand the mission, the goals, and the project objectives and how the team fits with the bigger picture.

Part I.D

Most of these steps are basic to effective project management. However, a significant portion of the impact of such initiatives also will be related to the quality of leadership demonstrated throughout the project. Following are some useful guidelines:

• Understand that all people—and organizations—involved will have their own priorities, perspectives, and skills. Learn what they are, recognize the validity of the differences, and find ways to integrate them effectively. • Be aware of your own strengths and weaknesses and how they can affect project success. Find ways to learn from and utilize the skills of others to compensate. Also, provide as many opportunities as possible for other project personnel to utilize their full capability and to develop new skills. • Communicate, communicate, communicate. People tend to fill gaps in their understanding with their own bias or fears, so keep the gaps to a minimum. • Be a role model by emphasizing and demonstrating the importance of high-quality work. Additionally, a quality engineer will frequently be called on for technical advice regarding particular methods for process analysis, such as conducting a process failure mode and effects analysis (see Chapter 17). Although they may not be in a leadership role, they must still understand these principles.



Part I.E

Chapter 5 E. Facilitation Principles and Techniques

Define and describe the facilitator’s role and responsibilities on a team. Define and apply various tools used with teams, including brainstorming, nominal group technique, conflict resolution, force-field analysis, etc. (Analyze) Body of Knowledge I.E

FACILITATOR ROLES AND RESPONSIBILITIES IN THE TEAM ENVIRONMENT Concurrent with the development of teams was the emergence of the facilitator as a key organizational player. Teams and facilitators go together like love and marriage, horse and carriage. Whereas the old fashioned “boss” would simply tell workers what was to be done, the facilitator must understand the objectives, needs of, and constraints on the team.

Purposes of Facilitation In an ideal world there would be no need for facilitators. Everyone would have the skills necessary for their roles and would work effectively with everyone else. However, it is not an ideal world, since all of us are continually learning. The role of facilitator is therefore a valuable one since it allows special additional skills to be readily available to the team. A facilitator’s primary mission is to ensure that a team is successful, but this must be done in a way that ensures that the team, not the facilitator, is responsible for the outcome. A really successful facilitator is one that is continually working him/herself out of the role through helping the team develop higher and higher levels of competency. The facilitator is termed a marginal role, since facilitators are not actually members of the team with which they are working. However, facilitators usually are present at most or all of the team’s meetings and their role is to provide support that helps the team work better. Simple examples of this support include 52


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notifying the team that they have veered off the meeting agenda, have jumped to a conclusion without any supporting data, or are not allowing all team members to voice their opinions.

Facilitators usually take one of two major roles with a team. One is that of meeting manager, whereby the facilitator is actually guiding the team through the agenda and flipcharting discussions that occur. The other is that of an observer, where the facilitator sits quietly to the side and simply comments when it seems necessary or useful to further team progress. The observer role also provides the opportunity to gain information that can be used to coach the team leader in team process skills. An important distinction, though, is that facilitators do not discuss content issues, only process issues. For example, if a team were trying to reduce the amount of time patients spend in the waiting room of a healthcare clinic, the facilitator would not interject comments such as, “Should we change the patient scheduling process?” since it is relative to technical content of the subject matter. However, at the appropriate time the facilitator might ask, “What are some additional ways that the time could be reduced?” since it only involves ensuring that the team has taken a broad view of potential opportunities. It is not necessary that facilitators be someone from outside the team. The team leader or a specific team member who has sufficient skills and experience may also take on the role of facilitator. In this case, the facilitator is allowed to contribute content, because the person is in fact a bona fide member of the team. The ultimate objective, of course, is for all teams to be fully capable of working without the need for anyone in a designated facilitation role. Each member simply pays attention to both content and process issues and ensures that the team works effectively.

Part I.E

Different Facilitator Roles

What a Facilitator Pays Attention To Because a facilitator tries to help the team be more effective, there is a wide range of issues to consider. Here is a list of just a few of the items that facilitators must pay attention to: • Meeting agenda. Is there an agenda for each meeting, and does the team follow it? • Communication. Do team members listen to and discuss each other’s opinions, or does each simply state his or her own? Are discussions on a positive note or does negativity sap people’s energy? Does everyone have the opportunity to speak, does the team leader appear to give more attention to some, or do some individuals dominate? • Technical process model. Has the team engaged in procedural conflict— negotiating the where, when, how, and why issues, such as defining the steps they are going to use to carry out the project (for example, a seven-step problem-solving model, if appropriate), or are they simply wandering around with no defined direction?



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• Conflict. Is there interpersonal or procedural conflict between group members that prevents them from working together effectively? Is all conflict being suppressed, which causes ideas to be withheld? Is substantive conflict—deferring consensus when discussing ideas to get to the best ideas—encouraged? • Decision making. Does the team make decisions based on data, or do they jump to conclusions? Is consensus used when the decision is one that requires everyone’s commitment? • Follow-up. Does the group identify action items, then ensure that they are carried out?

Skills Required of a Facilitator An effective facilitator must have a broad range of capabilities. Three of the most important are: • Meeting management skills. A facilitator should know how to run meetings in a manner that effectively uses the time available. In many ways, meetings are like mini-projects, with a mission (purpose of the meeting), technical process (meeting agenda), and boundaries (meeting duration). In addition, since meetings consist primarily of discussion, the ability to communicate effectively is vital. • People skills. Since each person brings his/her own background, skills, and priorities to meetings, the ability to understand and work with different perspectives is critical for a facilitator. An understanding of psychology (both individual and social) and methods for change (for example, from the field of organization development) are therefore valuable. • Technical process analysis skills. Improvement of processes involves analysis of processes. An understanding of the seven basic QC tools, the seven management tools, statistical process control, and design of experiments gives a facilitator a wide range of tools that can be introduced at an appropriate time. (These tools are all discussed in Parts V and VI.) Perhaps the most important knowledge for facilitators is also the most difficult to obtain: understanding themselves. It is difficult to understand others if you do not understand yourself, because you may make interpretations using filters of which you are unaware. An effective facilitator must be able to sort out the difference between whether a particular intervention is being done because of the needs of the team or the needs of the facilitator. If the latter, it’s being done for the wrong reason.

Ways of Intervening When facilitators believe that the team should change the way they are working, they can select from several different ways of bringing the need to change to the attention of the team. The particular method the facilitator chooses often will depend on a combination of the facilitator’s personal style, level of comfort with the team, and how the team has responded to previous interventions. Following are some of the different ways to intervene:



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• State observations. A slightly more discreet way of intervening is for the facilitator to simply state what he/she is seeing that the team may want to do differently. This puts the information in front of the team, allowing them to decide whether or not to pay attention to it.

Part I.E

• Tell them. The easiest way is simply to tell the team either what they are doing wrong or what they need to do differently. For minor issues this is a quick and probably safe intervention but may cause more resistance with some teams since it can be interpreted as being a bit authoritative.

• Have them explore. Another choice is to ask the team to think about what they are doing at the moment (and perhaps frame the context of the issue, for example, whether it is relative to communications or agenda issues). Although this method takes more time, it causes the team to take more ownership of the intervention, meaning that learning is more likely to be internalized. Perhaps it is clear from some of the above discussion but it is worth emphasizing again: it is vital for the team to have ownership of decisions that are made regarding content and, when possible, also of team process decisions. A facilitator who gets glory from making such decisions for the team simply reduces the likelihood of the team learning from and being committed to the team process. There are, however, situations when facilitators have a higher level of involvement than has been presented here. For example, with kaizen blitz teams, which typically last three to five days, acceleration of the improvement process comes about partially due to reducing concerns over how decisions are made. The facilitator in such projects usually has much more authority to specify the direction the team will take.

IDEA PROCESSING AND DECISION MAKING Most people are familiar with brainstorming as a means of generating many ideas in a short period of time to identify solutions to problems. Groups and teams can use both structured and unstructured brainstorming methods. For unstructured brainstorming, a topic is agreed on and written in front of the group. The leader/facilitator then asks for ideas to be randomly called out and all are recorded without any discussion. When the flow of ideas stops, the list is reviewed and discussed, which may result in the elimination or combination of some. A structured approach involves a round-robin process whereby each person in the group is asked to state one idea. If a person has none, he/she passes and the next person is asked, and so on. When everyone has passed on a round the brainstorming is complete. A similar process can be used by posting several sheets of paper around the room with a topic or problem at the top of each. Each team member goes to one sheet and writes down ideas that come to mind, then the members rotate repeatedly until all have contributed to each sheet. Another alternative is to simply circulate sheets of paper among the group. Another method of brainstorming, called Crawford slip, is especially useful when the team is working on a particularly sensitive topic or when the team does not yet have a high level of trust. All the team members are asked to record their



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ideas on pieces of paper that are then given to a trusted individual (for example, facilitator) who compiles all the items into a single list (for example, on a flipchart). The anonymous nature of this method helps people feel freer to include their ideas, and the team often finds that several members had the same idea, which begins to build cohesiveness.

Nominal Group Technique Nominal group technique is one way of processing lists of brainstormed items. It involves using the following steps to reduce a large list to a shorter one: 1. Ask each participant to rank the items in numeric order (for example, 1 is best to 8 is worst in a list of eight items). 2. Record the ranks of all participants beside each item. 3. Total the rankings for each item. Those with the lowest totals are the preferred options. Figure 5.1 shows an example applied by a group of course participants who were trying to decide where to go for lunch. Of the four choices, Marlow’s received the lowest total (therefore the highest priority) and was then the group’s first choice. Multivoting. Another way to narrow down a list of items is to have the group select from the list only those that they prefer. The number they are to select is usually approximately one-half of the total number. After all participants have made their selection, the facilitator asks how many participants voted for each option, and records this. The Pareto principle will usually work, with some of the options getting very few votes; they are then dropped from the list. The voting process is then repeated until the desired number of items remains. Figure 5.2 shows multivoting on a larger version of the lunch selection problem. Five people are voting, and in the third round of voting Grunge Café finally emerges as the winner by a 4:1 margin.

Resolving Conflict Most people identify conflict as a problem to be solved, as something that is inevitable—and undesirable—in teams and only comes about when two or more people have ideas that appear to be totally different and where it is perceived that

Individuals and rankings Restaurant

Tom

Joe

Mary

Sue

Terry

Total

Marlow’s

1

2

3

1

2

9

Grunge Café

3

1

1

2

3

10

Stew & Brew

2

4

2

4

4

16

Fancaé

4

3

4

3

1

15

Figure 5.1 Nominal group technique ranking table.



First vote (select 4)

Second vote (select 3)

Pizzas R Us

2

Marlow’s

4

Alice’s Restaurant

1

Grunge Café

5

Mom’s Diner

0

Stew & Brew

3

2

Fancaé

5

5

Third vote (select 1)

3

5

4

Part I.E

Restaurant

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1

Figure 5.2 Multivoting.

a choice must be made between them. In reality, however, two kinds of conflict— substantive conflict and procedural conflict—can actually enhance teamwork. A third kind, affective or interpersonal conflict, results when team members “allow personal feelings to negatively affect group interaction” (Burnett 2005), such as when hidden biases surface, normally inconsequential behaviors become irritants, or past slights or unresolved issues spill over into team interaction. Dealing directly with affective conflict means that a facilitator or other team member either reminds the team of its common goal or puts the grievances on the table in as neutral a fashion as possible to defuse the situation or negotiate a compromise that will allow the team to function. Negotiation is a key to resolving procedural conflict, especially when a team is first convened, at key points in reaching an objective or goal, and at the beginnings of project meetings. As the name suggests, procedural conflict has to do with how the group runs, and requires participants to be very clear, to write down and maintain group memory documents that keep track of where and when the team will meet, who will take on certain roles (such as team leader, recorder, time manager, devil’s advocate), what procedures and tools the team will use (such as consensus versus voting, flipcharts versus an intranet), and the anticipated time line for meeting the team’s objectives. All of these issues are important and some may need to be renegotiated on an ongoing basis to keep the group running smoothly. By engaging in substantive conflict, teams actively work at avoiding hasty consensus (such as jumping on the first idea instead of waiting for possibly better alternatives or making a decision before everyone has had a chance to give input). Teams can use three strategies to defer consensus: • Elaborate key ideas by adding details, examples, or explanations. Remember that one good idea can spark several other good ideas, which means the team has more choices. • Consider alternatives by adding to an idea or exploring an idea that has not been previously considered. One team member might add details to help another explain a suggestion or might restate the idea so that everyone understands.



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• Voice disagreements to strengthen the product or process. Remember that disagreeing does not mean you do not like someone; in fact, disagreeing about ideas can mean that you are sufficiently engaged to notice strengths and weaknesses (Gillette et al. 1993). The following guidelines incorporate each of the three kinds of conflict: • Encourage people to exchange ideas freely before coming to a decision • Treat the discussion as a problem to be solved instead of an attack on a person • Take the time to attend to housekeeping issues such as regular breaks, room temperature, and sufficient supplies of necessary items (paper, pens, tissues) • Consider—and keep records of—the benefits and drawbacks of each option • Keep the team’s goals and objectives—the team’s common interests— on the front burner, especially when tempers run high One difficulty is getting everyone on the team to really understand both what the others are saying and why it is important to them. When everyone understands and is willing to share their values and the assumptions underlying their positions, asking team members to restate in their own words what has been said helps ensure true understanding. Time also is an ally for conflict resolution. If the issue is over a decision that can be delayed, the time between subsequent discussions may allow the players to not only cool off, but also to think over both their own positions and those of other team members. When all is said and done, many of the skills related to conflict are also communication skills.



Chapter 6 Part I.F

F. Communication Skills

Describe and distinguish between various communication methods for delivering information and messages in a variety of situations across all levels of the organization. (Analyze) Body of Knowledge I.F

Every communication interaction is unique in terms of purpose, context, mode of communication, and people involved. Therefore, within this section the terms audience, reader, listener, and customer are used interchangeably as are the terms speaker and writer.

THE NEED FOR COMMUNICATION SKILLS Communication skills are essential for success whether measured by promotion or by higher-quality processes and products. Only the rarest job does not require excellent communication skills. In the quality field, effective communication is essential in order for everyone to understand and have a sense of ownership of the common vision. Every employee must be aware of objectives and necessary actions that are required for successful quality initiatives within the organization. Common goals are a unifying factor in virtually all successful teams. The complex communication skills required to accomplish complex goals and objectives require comprehensive understanding of communication theory and practice. Communication is a key to leadership. Leaders must establish a vision, communicate that vision to those in the organization, and provide the tools and knowledge necessary to accomplish the vision. So good leaders understand and employ efficient and effective communication in order to achieve this goal. Remember that leadership is needed at all levels of the organization.

CREATING A SHARED VISION In order to accomplish a stated goal, all members involved in reaching that goal must understand and be committed to achieving that goal. One way to achieve 59



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understanding and commitment is to include all members in the complete process. Members of effective teams feel some ownership of programs and projects when they understand goals, objectives, and/or mutually well-understood expectations and are given access to needed information and resources. To create understanding and commitment, leaders employ skills such as clear formulation of a concept, emphasis of key points, repetition, and summarization. Multiple channels are absolutely vital to convey our message in the intricate information world we inhabit. Every listener/reader is bombarded with communication from myriad sources all day (and most of the night) long. Types of written communication include queries, directives, memos, summaries of meeting, formal and informal letters, planning agendas, invitations, apologies, e-mail and public documents. The use of paper (hard copy) is still sometimes required for archival and legal reasons. Clear and unambiguous writing is an essential skill. Practice and seeking out of constructive feedback are essential. Types of oral communication include interviews, formal speeches, conversation, debate, directives, briefings, and public announcements. Every successful quality engineer will master both discussion skills and presentation skills. The ability to analyze and organize information and to present this information orally will consistently reap rewards.

COMMUNICATION PROCESS The term “to communicate” comes from the Latin communicat(us) meaning to impart or make common. When we communicate we try to establish a coming together or common ground with someone. We share information, ideas, and attitudes in an attempt to establish a link or joining together with another. We give or exchange thoughts, feeling, information, and ideas. However, the communicator must know something about the receiver in order to link the message with the receiver. The message must be joined from the sender to the receiver by thought, word, or deed in order to facilitate comprehension followed by action. The greater the areas of common experience and understanding that the sender and the receiver share, the greater the possibility for successful communication. This means that communication is greatly enhanced by repetitive contact and sharing. Human communication is dynamic and ever changing. It is irreversible in that once it is transmitted and received it can never be totally forgotten or erased from memory. It is interactive in that it must be shared. It exists within a context both social and cultural, never in a vacuum. Our ability to communicate is the strongest force that makes us human beings and should be treated with utmost respect.

Aristotle The importance of communication to human interactions has a long history dating back to the time of Aristotle in ancient Greece about 550 BCE. His book The Rhetoric defined rhetoric as “the faculty of discovering in any given case all the available means of persuasion.” Rhetoric was the most powerful technology of his time because most communication was spoken. Carefully crafted rhetoric gradually replaced physical combat as the most effective way to persuade others to


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change their behavior. Decisions were made and actions were taken because of the strength of a given speech. Although the means by which we communicate have expanded to include written, visual, and electronic means, understanding the three basic rhetorical principles as set forth by this ancient Greek scholar can help quality professionals communicate. The three interrelated and equally important dimensions of the rhetorical process are ethos, logos, and pathos. These principles are equally important to both oral and written communication. Ethos. Often defined as credibility but has a much fuller meaning. The dimensions of credibility are competence, character, goodness, decency and trustworthiness, composure, sociability, dynamic extroversion, and a sense of purpose. (Effective communication is deep, subtle, and complex!) Logos. Logic, evidence, sequence of thought, building up of the case in a pleasing manner that enhances comprehension. It means giving an idea order and form. Pathos. The appeal to emotions. It implies a reaching out to our common bond of feelings and our innate sense of being human. It comprises our compassion, our values, and feelings about ourselves and others. The use of pathos has the ability to create in us a deeply felt response. These three dimensions cannot be studied or learned in isolation because they are tied together and of equal importance in attempting to persuade another to understand and accept the message being presented. Effective communication relies on these three dimensions—credibility, logic, and emotion—being blended together in a coherent way. As a quality engineer you rely heavily on the discovery and organization of data, facts, and evidence—systematically collecting, analyzing, and organizing the material (logos). In addition, your authority, expertise, character, and reputation enhance the believability of your message (ethos). When this message is then framed to appeal to the emotional state of your receiver (pathos), you have a compelling triad. An effective speaker is trustworthy and believable, has a well-organized, factual (coherent) message, is respectful to the listener, and appeals to their most salient feelings on the topic. All three methods of appeal—ethos, logos, and pathos—work together to promote the acceptance of the message. Once the message is accepted, cohesion (sticking-together) occurs in the mind of the listener, and the shared vision can be realized.

Active Listening Hearing what is said is not the same as actively listening to what is said. Hearing is simply the act of perceiving the sound and is largely involuntary. Listening is a selected activity that involves the reception and the interpretation of the sound and decoding of the sound into meaning. Active listening is much more difficult than one would assume and requires effort and concentration. The sender of a message has the responsibility to use all available means to construct the message that will have the best possible opportunity to be adequately received and understood as intended. The receiver has the responsibility to be open and actively ready to receive and attempt to comprehend the message. This exchange



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is the essence of communication. This shared responsibility is marked by active participation on both sides. Individuals speak at 100 to 175 words per minute but they can listen intelligently at 600 to 800 words per minute. Since it is possible to receive so much faster than the message can be produced orally there often is a tendency to allow our thoughts to drift to other things. This is why it takes effort and focus to hold attention on the message being sent. Active listening means to listen with purpose and concentration. The receiver decides to listen closely to gain information, obtain direction, understand others, solve problems, share interests, see how another feels, show support, and so on. It requires as much energy to listen actively as it does to construct and send the message.

Dimensions of Active Listening Dimensions of active listening include listening to understand, confirm, support, clarify, and diminish defensiveness. Listening to understand will not always mean agreement. Rather it means trying to grasp fully what is being sent from the point of view, feelings, and experiences of the sender. It involves paraphrasing and perception checking, which includes saying in your own words how you have interpreted the other person’s ideas and feelings and asking if your statement is correct. This is done to ensure that your understanding is accurate. Listening to confirm includes behavior that indicates to the sender that you are attending to the message and accepting the point of view expressed even though you may not agree. Your actions indicate that you value the person and the message that is being transmitted. This is frequently done while the message is being sent by nonverbal means such as looking at the sender, nodding, and positive facial expressions. Diminishing defensiveness as a listener also is accomplished nonverbally while the message is being sent by refraining from turning away, closing your eyes, agitated movement of your body, or negative facial expressions. Immediate verbal evaluations or interruptions of the message also will create defensiveness in the sender and should be avoided. Active listening requires the listener to hear the message, understand the meaning, and then verify the meaning by offering feedback both verbally and nonverbally.

Feedback Feedback is an important component of the communication interaction. It provides the opportunity for clarification and in-depth understanding. There are five main categories of feedback, listed following in the order in which they most frequently occur in communication exchanges: 1. Evaluation. Making judgment about the worth, goodness, or appropriateness of the statement. 2. Interpretation. Paraphrasing or perception checking as a means of clarification. 3. Support. Confirming behavior that encourages the sender to continue to communicate.


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4. Probing. Attempting to gain additional information, continue the discussion, or clarify a point.

Of all these five feedback methods, evaluation is the one that must be used with the most care. Insensitive evaluation will create defensiveness in the sender and may break the communication process. There are several ways for the listener to diminish this potential defensiveness:

Part I.F

5. Understanding. Trying to discover completely what the sender of the message intends or means by the message.

1. Limit negative evaluations 2. Keep evaluations honestly positive 3. Postpone specific evaluations 4. Keep evaluations tentative 5. Own your own statements 6. Ask for responses to your evaluations Being an active listener and supplying adequate feedback provide the important other half of the communication process. It holds equal responsibility with the sender for the successful transaction. It is what allows the message to be accepted and the unifying vision to be implemented.



Part I.G

Chapter 7 G. Customer Relations

Define, apply, and analyze the results of customer relation measures such as quality function deployment (QFD), customer satisfaction surveys, etc. (Analyze) Body of Knowledge I.G

Customers can be found both internally and externally to the organization, and you must find some way of communicating with your customers on a regular basis. In studies conducted for a number of years, Collins and Porras (1997) point out that the best-of-the-best companies (visionaries) in their respective industries have developed systems that transcend dependence on any single leader or great idea to build an enduring, great human institution that has lasted and will last for decades. Many of these companies have stumbled along the way but somehow find a way to come back, providing the customer or client the products or services that are wanted and/or needed. The true secret seems to be to try a lot of things, keeping those that work and stopping those that do not, and continually checking back with the customer to see if anything has changed, thus starting the process over.

CUSTOMER NEEDS AND WANTS Your organizational objectives should be to ensure that customers want and need your products and/or services. As Perry (1998) states, “Staying in direct, face-toface contact with customers, in their world, is the surest way to combat organizational myopia.” Far too often, a system is developed and people in that system “expect” customers to conform to the way things are done by the supplier organization. This occurs everywhere from the corner grocery store to other retail outlets, from schools to manufacturing organizations. How often have you seen cartoons with the central theme of “if it wasn’t for the unrealistic customers, this would be a great place to work?” The quality engineer’s job (either manufacturing or service based) is to help the organization see that the customers are their reason for existence, versus the other way around. This goes beyond just collecting a sample of information 64


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(surveys, focus group meetings, plant visits, and so on). Everyone has seen the customer survey cards at hotels and restaurants that ask about customer satisfaction. But what is the validity of such an effort when considering issues such as response rate and nonrandomness of response? A four-stage model for evaluating training events devised by Kirkpatrick (1998) (discussed in more detail in Chapter 15) would categorize this kind of data-gathering effort—and its validity—as reaction, or level one evaluation. Some consider these tools to be “smiley sheets,” a pejorative term referencing the halo effect that has been noted in research resulting from the glow of the moment of the event or because the participant wants the researcher to feel good. The real question for the quality engineer should be, “What do my customers think after using the product or service for some period of time in actual real-world settings, and what are they telling other people about my organization?”

QUALITY FUNCTION DEPLOYMENT Quality function deployment (QFD) is a powerful planning technique, perhaps the most comprehensive ever invented for quality planning. QFD is especially suited to large-scale products such as airplanes, automobiles, and major appliances. These products have heavy tooling, high design costs, and many optional features that must be selected and then produced or procured. QFD was introduced into American industry in the 1980s by the American Supplier Institute of Livonia, Michigan, which remains one of the organizations that actively promotes its usage.

Definitions and Concepts of QFD The six key terms associated with QFD (Sullivan 1986) are: 1. Quality function deployment. An overall concept that provides a means of translating customer requirements into the appropriate technical requirements for each stage of product development and production (that is, marketing strategies, planning, product design and engineering, prototype evaluation, production process development, production, and sales). 2. The voice of the customer (VOC). The customers’ requirements expressed in their own terms. 3. Counterpart characteristics. An expression of the customer’s voice in technical language that specifies customer-required quality. 4. Product quality deployment. Activities needed to translate the voice of the customer into counterpart characteristics. 5. Deployment of the quality function. Activities needed to assure that customer-required quality is achieved; the assignment of specific quality responsibilities to specific departments. (Note: any activity needed to assure that quality is achieved is a quality function, no matter which department performs it.)




Part I.G

6. Quality tables. A series of matrices used to translate the voice of the customer into final product control characteristics. Sometimes it is possible to incorporate all of the key relationships into a simple diagram called the house of quality because of its distinctive shape. Figure 7.1 shows such a diagram, which resembles a house with a pitched roof. For comprehensive coverage of more than 30 different planning tools grouped under QFD, see King (1987). A typical project will require only a few of these. The following QFD documents are most common: 1. Customer requirements planning matrix 2. Design matrix 3. Final product characteristic deployment matrix 4. Manufacturing/purchasing matrix 5. Process plan and quality control charts 6. Operating instructions

Paperwork SPC

Corrective action program

Follow-up action plan

Relative

1

3

2

4

Absolute

1

3

2

4

Strong Medium Small

Customer importance

Fast disposition

Relationship:

Next day disposition Paperwork correct Responsive follow-up Corrective action Difficulty/cost

WTS

Key elements

Figure 7.1

QFD house of quality diagram for a paperwork process.



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Application of QFD: The Basics

Part I.G

By applying QFD, customers’ expectations are translated into directly related job requirements. The objective is improved customer satisfaction at acceptable cost. The basic relationship is displayed in the input–output matrix shown in Figure 7.2. This matrix—only one of many in QFD—organizes the process of determining relationships between what the customers want (usually described in nontechnical terms) and how the supplier satisfies these wants. Wants fall into three categories: must have, expected to have, and would like to have. Numerical measures are highly desirable. The wants must be specified in sufficient detail to ensure they are clearly understood. Although customers may or may not be involved in setting the requirements, their satisfaction will depend on identifying and meeting their wants. The hows are the technical details of each job. The strength of each relationship may be strong, medium, or small, as shown in Figures 7.1 and 7.3. These symbols can be converted to weights, such as strong = 5, medium = 3, and small = 1. The weights will convert to scores indicating how important each job requirement is. At the top of the requirements matrix, a correlation matrix is added to show the strengths of the relationships among the different job requirements. A small example is shown in Figure 7.1 for a paper improvement project, and a more complex example of a car door design is depicted in Figure 7.3. QFD as a planning technique has brought significant benefits: 1. Product objectives based on customer requirements are not misinterpreted at subsequent stages. 2. Particular marketing strategies or sales points do not become lost or blurred during the translation process from marketing through planning and on to execution. 3. Important production control points are not overlooked.

"Hows" (Job requirements)

4. Efficiency is increased because misinterpretations are minimized.

Relationship matrix

"Whats" (Customer requirements)

Figure 7.2

Input–output requirements matrix.




x x Correlation: Strong +

Strong Medium Small Max

Operating efforts

7 5 2 2 5 6 5 6 3 2 6 6 6 8 7 5

Better Technical competitive benchmark Worse Service repairs/1000 cars Service costs: $/car Reg: hinge strength Reg: door crush Reg: lock strength Door off assem Rabbet control Plastic outer panel Absolute Technical importance Relative

1 2 3 4 5

2 5

35 4 8 11 15 12 4 5 1 1 3 1 1 3 3 4

Objective target values

Customer rating

45

2 1 5

1 1 3 5 4 0 0

7.5 ft lbs 7 lbs max 10 lbs 7–15 lbs 20 ft lbs max 5 lbs max 4 lbs max 5 in lbs max 20 in lbs max 26 in lbs max Target area 7mm min CW/CCW 10–40 MW 25mm MW AC Target area Target area Target area Target area 4 minute test

1.0 Good operation and use 1.3 Latch 1.2 Window 1.1 Easy to lock easy open easy Open/Close 1.4 Leak

Lock

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Importance 1.1.1 Easy close OS 1.1.2 Easy close VS 1.1.3 Easy to open 1.1.4 Doesn't kick back 1.1.5 Stays open in chk 1.2.1 Easy crank reach 1.2.2 Crank easy hold 1.2.3 Easy to operate 1.2.4 Wipe dry on oper 1.2.5 Operates rapidly 1.3.1 VS lk knob ease 1.3.2 Key insert easy 1.3.3 Key operate easy 1.3.4 Does not freeze 1.4.1 No water leak 1.4.2 No drip w/open Technical difficulty

Human factors Lock/ Window latch

Dr close effort Rem hdl opn eff O.S. hdl opn eff Static hold opn Dynam hold opn Vs lk btn unkl eff Key insert effort Keyn unlk effort Meet freeze test Wdw eff up/dn Crank location Crank offset Direct. of oper Knob size (diam) Remote hdl shape Remote hdl loc. Vs lk button loc. Mir Oys mir hdl loc. Dr pull hdl loc. Dr Does hot leak Service complaints Door

Customer requirements

Important control items

+ – Strong –

Target

Design requirements

Figure 7.3

x x

Better

Max

x

Worse

Part I.G

Relationships:

A Car B Car C Car

5 4 3 2 1 56 48

5 81

14 28

10 43

25 28

18 56

108 18 18 73 57 54 60 60 180 65 77 21 20 51 6 18 18 10 2 2 7 5 5 6 6 17 6 7 2 2 5 1 2 2

17 28

45 10 4 10

House of quality for a car door.



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Customer Value Analysis Gale and Wood (1994) describe seven tools of customer value analysis:

2. The market-perceived price profile (a weighted indicator of how customers perceive different competitors’ performance on given price attributes)

Part I.G

1. The market-perceived quality profile (“indicator of how well you are performing overall for customers in your targeted market”)

3. The customer value map (a “map that reveals a sizable cluster of business units receiving premium prices that are not fully supported by superior perceived quality”) 4. The won/lost analysis (an analysis of those factors that won or lost the sale) 5. The head-to-head area chart of customer value (a “chart of customer value displaying where you are doing well and where you do worse against a single competitor”) 6. The key events timeline (a chronological list of the events which changed the market’s perception of performance on each quality attribute, yours and your competitor’s) 7. A what/who matrix (“a method for tracking who is responsible for the actions that will make success in customer value possible”) Using these tools will “enable an organization to navigate strategically even in confusing times. . . .” Numerous factors represent value to different customers under a variety of situations. The characteristics shown in Table 7.1 illustrate different perspectives on what the customer considers important.

Table 7.1

Customer perspectives of value.

Characteristics—product (examples)

Performance Reasonable price Durability Safety

Serviceability Ease/flexibility of use Simplicity of design, aesthetics Ease of disposal

Characteristics—service (examples)

Responsiveness Reliability Competence Access Courtesy Communication (sensitivity, genuine interest/concern)

Credibility/image Confidentiality/security Understanding the customer Accuracy/completeness Timeliness




Part I.G

CUSTOMER-DRIVEN QUALITY A growing number of approaches focus on greater understanding of and interaction with customers. The two types of customer-driven quality, reactive and planned, are proving to be successful in improving quality but still do not guarantee customer satisfaction (Foster 1998). Reactive customer-driven quality (RCDQ) responds to customer requirements after the fact. Planned customer-driven quality, on the other hand, is anticipatory and proactive in that it assesses customer needs and seeks methods for satisfying those needs before the fact. Any organization wanting to meet customer expectations is pursuing a moving target. The reactive nature of the RCDQ approach will cause the supplier to fall behind the moving target. Planned customer-driven quality is best accomplished using some form of strategic quality planning (SQP). This is not necessarily the same as the strategic planning process, however, and is one reason that the Malcolm Baldrige National Quality Award changed the name of the SQP category to strategic planning to counter the sense that some quality professionals had too narrow a focus on company competitiveness in the marketplace. With any given effort to become a customer-driven company, an organization needs to study what they do and how they look to their customers. One list of top 10 key characteristics of customer-focused companies includes: 1. Total consumer experience. The ability to look at the customer from all angles of how the organization’s products and services are experienced in the real world. Look for every possible point of contact with the customer to collect information on what is happening in the field. 2. Product hits. Use of the Kano model to continuously delight the customer with new products and services, some of which the customer may not even have known that they wanted. 3. Consumer loyalty. Building a sustained momentum over time to the point where the customer will only use your product or service, even waiting, if necessary, to get the “real thing.” 4. Retailing and distribution. Creating a win–win–win for your organization, distributors, and customers. Your distribution system is a customer as well. 5. Brand process. The creation of recognized products or services that are sought after in the marketplace. 6. Logistics. Providing just-in-time and just what is needed/wanted in the marketplace at point of usage. 7. Build to demand. Creating a lean process that is capable of rapid changeovers to give the customers the needed products and services as they want them (just in time). This process has to be built into the entire system from suppliers, through production to the ultimate customer.



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8. Consumer knowledge system. Continuous information gathering of customers’ expectations and wants that feed into the system; used to look for continual improvement opportunities.

10. Growth. Continually improving with faster service, better value, and higher quality to create a culture that uses creativity and innovations to improve customer satisfaction.

Part I.G

9. E-commerce. Becoming interactive, offering distribution, selling, and constant communication with customers online.

To summarize this chapter: there is no sure way to always satisfy or delight customers because we cannot talk to every individual customer that we have and because customers are constantly changing their minds about what they need or expect. So we must find ways to continuously talk with many customers using the techniques we can. With today’s technology this should become easier, but will the quality engineer be able to ensure that the information received is good enough to make sound predictions? The challenge is to keep the process both simple and informative. (See Chapter 2 for more details on quality information systems.)



Part I.H

Chapter 8 H. Supplier Management

Define, select, and apply various techniques including supplier qualification, certification, evaluation, ratings, performance improvement, and so on. (Analyze) Body of Knowledge I.H

Many years ago, companies worked under the assumption that engineers designed products and specified requirements, suppliers provided materials, manufacturing built the products, and quality control inspected the product after it was made to assure quality. This approach was inherently wasteful. Beginning in the 1940s, the use of quality standards for suppliers has gradually evolved into a system that assures quality products that meet requirements with only a limited amount of inspection by quality control personnel. MIL-Q-9858, BS 5750, industryspecific (starting in the early 1960s), and ISO 9000 standards (see Chapter 12) have each made their contribution. Quality assurance personnel now spend greater effort assuring that quality is built into products and that conformance is achieved during production. The lines are becoming more blurred as Six Sigma programs help everyone in the organization become concerned about quality and defect prevention. The same team cooperation and close communication used internally are now being applied to supplier relations. The goal is to assure that purchased items and materials conform to requirements without the need for extensive inspection upon receipt by the purchaser and that continual improvement is being practiced (Johnson and Webber 1985). Suppliers also can be found both internally and externally to the organization, so the best advice is that you must find some way of communicating with all of your suppliers on a regular basis.

PROCUREMENT STANDARDS AND SPECIFICATIONS Standards and specifications are documents containing criteria that must be met, and these documents become legally binding by reference on the purchase order. They define what is being purchased. They can be in the form of engineering 72


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drawings, catalog descriptions, or other documentation. It is important that the applicable standard or specification document be incorporated into the purchase order so there is no doubt that the requirements are to be met. If they are not incorporated, there is no basis for enforcing compliance. The purchaser need not always develop original specifications. Commercial quality specifications are available and range from detailed engineering drawings (which may include references to process specifications, such as reliability verifications and inspection requirements) to off-the-shelf items (which are defined by the characteristics on the manufacturer’s data sheet or catalog). Such commercial specifications help simplify the procurement process.

SURVEY VERSUS AUDIT VERSUS SAMPLING INSPECTION At the superficial level, surveys, audits, and inspection are all about the same— they provide internal or external customers with a degree of confidence, but not absolute assurance, that the quality of the product or process is what it should be. However, each of these tools has its own distinctive characteristics.

Survey The survey can be defined as a broad overview of a supplier’s system and/or processes that is used to evaluate the adequacy of that system or processes to produce quality products (LaFord 1986). The system survey is used to assess whether the supplier has appropriately controlled systems that will adequately prevent the manufacture of nonconforming products. The process survey is used to evaluate whether a supplier has controls in place to ensure that the process will manufacture quality products. Process controls include proper tooling, equipment, inspection, and so on.

Audit An audit can be defined as a systematic examination of the acts and decisions with respect to quality to independently verify or evaluate compliance to the operational requirements of the quality program, specifications, or contract requirements of the product or service (American National Standard 1978a). Note that the term compliance, often meaning compliance to documented procedures, is used instead of the term adequacy. Audits of a supplier’s systems or processes can only be performed at the supplier’s facility. Audits of a supplier’s product may be performed either at the supplier’s or customer’s facility. The system audit is a documented activity performed to verify, by examination and evaluation of objective evidence, that applicable elements of the quality system are suitable and have been developed, documented, and effectively implemented in accordance with specified requirements (American National Standard 1978a). The process audit is an analysis of elements of a process and appraisal of completeness, correctness, or conditions, and probable effectiveness. The product audit is a quantitative assessment of conformance to required product characteristics. Simply stated, the product audit verifies that the system and processes used to produce the product are capable of producing a product that conforms to the established specifications/requirements. This should not be




Part I.H

confused with the term inspection, which concerns the acceptance or rejection of the product or lot.

Sampling Inspection Inspection is a process of measuring, examining, testing, gauging, or otherwise comparing the unit with the applicable requirements. Sampling inspection is somewhat comparable to survey and audit; 100 percent inspection is somewhat comparable to production line operation because each and every item is subjected to it. (See Chapter 23 for inspection and sampling.) One hundred percent inspection is required in certain highly critical processes, and in processes that produce unavoidable defects, such as semiconductor fabrication. However, both Deming and Juran point out that 100 percent inspections done by humans are usually only around 80 percent effective. Thus in today’s industrial environment, 100 percent inspections are nearly always automated. Acceptance sampling is sampling where decisions are made to accept or reject a product or service based on the results of inspected samples. Skip-lot inspection is an acceptance sampling plan in which some lots in a series are accepted without inspection because the sampling results for a stated number of immediately preceding lots met stated criteria. Explication of this methodology is found in American National Standard, ANSI/ASQC S1-1987. Incoming inspection is the inspection of purchased parts at the customer’s facility, after the shipment of parts from the supplier, to ensure supplier compliance with specifications and contractual agreements. Source inspection is the inspection of purchased parts at the supplier’s facility by a customer representative to ensure supplier compliance with specifications and contractual agreements.

SURVEYING THE SUPPLIER The primary purpose of a survey of a supplier or potential supplier is to ascertain whether the supplier has: adequate financial resources (evaluated by purchasing), adequate manufacturing capabilities (evaluated by manufacturing engineering), and adequate quality systems (evaluated by the quality assurance group). In preparing for the survey, the team leader should obtain as much information about the supplier as possible. The purchasing agent can provide copies of the supplier’s annual reports, credit investigation, Dun & Bradstreet reports, Internet searches, and so on. A facilities and equipment list should be obtained for review by manufacturing engineering, and a copy of the supplier’s quality manual must also be reviewed prior to the survey. The survey team may be made up of members from purchasing, manufacturing, and quality control, plus various specialists in the areas of nondestructive testing, product design, or other special processes. At times, the team may consist of only the quality professional. In the latter case, the purchasing agent usually has previously evaluated the supplier’s financial status. It is important that the team meet prior to arriving at the supplier’s facility. Based on the premise that the team has reviewed all pertinent materials, the presurvey meeting is held to: (1) assure that all of the team members agree on



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the theme and purpose of the survey, (2) assure that the roles and responsibilities of each team member are understood by the others, (3) draft a preliminary survey agenda, and (4) select the team leader. This meeting is too important to be scheduled at the last minute in the airport or in the hotel the evening prior to the survey. The team leader must not overlook the obvious, such as the supplier’s current address, name of host individual to contact, correct time and date for the survey, and so on. It is important that the team leader verify that the supplier is ready for the survey. Often it is appropriate to advise the supplier of the proposed agenda, allowing supplier representatives to prepare for the visit. In order to quantify the results of a survey, there must be a formalized approach to collecting and evaluating the systems observed. The primary method of quantification is for the survey team to use a checklist(s) to record survey results. Checklists commonly used cover both procurement and manufacturing/quality aspects of a supplier’s organization. The manufacturing/quality checklists often are broken into the following categories: 1. Drawing and specification control 2. Purchased material control 3. Measuring and test equipment control 4. Process control and product acceptance 5. Material storage area, packing, shipping, and record retention control 6. Quality program management 7. Statistical process control 8. Strength summary of system survey 9. Corrective action summary of system survey 10. Summary report The manufacturing/quality categories may be expanded as needed. An amplification of the listed categories can be found in Laford (1986). The supplier procurement checklist often is broken down into the following categories: 1. General information 2. Product information 3. Facilities and equipment information 4. Sales, shipping, and payment information The supplier procurement checklist categories may be expanded as needed. An amplification of the list can be found in Laford (1986). The use of scoring (numerical, alphabetical, or other regularly sequenced scores) in a checklist further enhances quantification and validity of judgments. Many professional evaluators prefer to have the supplier also score a copy of the



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checklist in order to better compare the customer’s viewpoint with that of the supplier’s. The opening conference is get-acquainted time. The survey team members should explain why they are there, what they are going to attempt to do, and, in a general way, the sort of results they expect. Each team member should explain his or her role in the survey and in the customer’s organization. The team leader also should briefly explain the nature of the customer’s products or services. It is essential that all levels of supplier management understand the scope and purpose of the survey (Vendor-Vendee Technical Committee 1977). Each supplier representative present should explain his or her role in the supplier organization. At this time, the supplier representatives also should briefly describe the nature of the products manufactured and present an overview of the company and systems used. The opening conference also is a good time for the survey team to brief the supplier on the intended products to be purchased. A brief plant tour will acquaint the survey team with the supplier’s overall operations. Following the plant tour, the team members can proceed to their respective areas for evaluation. Each area should be evaluated in detail in accordance with the checklist and point scores recorded. It is imperative that each area be evaluated in the actual area and not in the conference room or manager’s office. Furthermore, by being in the appropriate area, verbal statements of compliance and quality procedures can be verified by witnessing the action being performed. The survey team should discuss any negative findings with the supplier escort who was present during the finding to reconfirm the facts prior to the closing conference with supplier top management. Prior to the closing conference, the survey team must meet to compile the report for that conference (this is not the final report). During the closing conference, the team leader should review each category, expressing the strengths and weaknesses observed. At this time it may be possible to estimate corrective actions required for deficiencies found if they have not already been addressed. The closing conference must be kept on a positive note, with a win–win attitude on both sides, which requires careful attention to communication strategies and can challenge the team leader’s communication skills. In the closing conference, the team leader should focus on the major deficiencies found, if any, and detail appropriate corrective actions. This should be followed by a brief mention of any minor deficiencies observed. All can be lost if the survey team presents an extensive list of minor observations with a few major deficiencies intertwined. If at all possible, the survey team should leave a draft copy of the survey report with the supplier. By doing so, any questions can be cleared up immediately. It is much more difficult to clarify misunderstandings when a copy of the final report is received a month—or more—later. The end product of the survey or quality program evaluation should be an understandable final report. A good report effectively communicates the findings, using the original observations to support the conclusions. The report must be an honest, objective summation of the team’s efforts. The report should detail the following: 1. List all individuals present and their correct titles 2. List the areas evaluated



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3. List any major deficiencies requiring written corrective action 4. List any minor deficiencies

6. A closing statement expressing appreciation for the supplier’s assistance and cooperation

Part I.H

5. A summary that states the final conclusion, for example, approval, conditional approval, or disapproval

Survey follow-up is carried out to assure that satisfactory corrective action has been taken by a supplier that did not qualify at the time of the survey visit. The customer may have to judge if a follow-up visit is warranted. A report from the supplier, accompanied by suitable documentation of corrective actions taken, may be adequate.

SUPPLIER RATING AND EVALUATION Rating a supplier’s capabilities is a twofold process: (1) rate or evaluate the supplier’s system (financial, manufacturing, and quality), and (2) rate the supplier’s delivered product. The rating of a supplier’s system usually begins with the initial supplier survey (discussed earlier). Often, the initial survey is followed up with a periodic supplier resurvey, called a systems audit. The audit provides the customer with an opportunity to evaluate the supplier’s systems over time so that any deterioration is noticed immediately. The rating of a supplier’s delivered product basically takes the form of recording, in some predetermined manner, the results of incoming inspections. It also can include failures caused by the supplier’s delivered products that appeared during the customer’s manufacturing cycle or while the product was in service.

Elements and Formulas Supplier rating elements and formulas are as diverse as companies are. The common aspects are quality, price, and delivery. The quality factor usually includes: • Quality lot rating Quality lot rating =

Number of lots rejected Nu mber of lots inspected

• Quality part rating Quality part rating =

Number of parts rejected Number of parts inspected

• Comparison to competition • Complexity analysis • Economic conditions




The delivery factor usually includes:

Part I.H

• Timeliness rating • Completeness rating The timeliness rating is based on the due date of the lot minus some demerit (for example, 10 percent) for each day the lot is early or late beyond some specified grace period or window (for example, due date ± two working days). It is important to note that if the supplier chooses the freight carrier, the system can base the due date on the date the lot is received on the customer’s dock. If the customer chooses the freight carrier, however, the due date should be measured by the date shipped from the supplier. The completeness rating =

Number of parts actu ally received Number of parts scheduled to be received

An overall rating can be derived by assigning percentages to the aforementioned aspects of quality, price, and delivery. Quality lot rating—40 percent Quality part ra a ting—60 percent

⎫ ⎬ Equals quality rating ⎭

Comparison level—40 percent Complexity leevel—30 percent Economic condition—30 percen nt

⎫ ⎪ ⎬ Equals price rating ⎪⎭

Timeliness ra ting—50 percent Competence rating—50 percen nt

⎫ ⎬ Equals delivery rating ⎭

The next step is to assign weights to the three main factors. For example: Quality rating—40 percent Price rating—30 perrcent Delivery rating—30 percent

⎫ ⎪ ⎬ Equa ls overall supplier rating ⎪⎭

This generic example can be expanded into an elaborate computerized system. It also can be tailored for use by smaller businesses that may still have manual systems.

Supplier Monitoring The purchasing organization usually tracks and monitors suppliers. A special supplier quality assurance (SQA) group may be formed to work with the buyer to look at suppliers’ performance. Some common supplier information includes: Defective parts per million (PPM) Cost adjustment requests



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Delivery date slippages Performance improvement

Part I.H

Using metrics such as the above, a quality information system (QIS) can generate reports such as supplier profiles by select criteria. Suppliers can be ranked by PPM, improvement, or similar metrics. Preferred suppliers can then be selected using quantitative data instead of guesswork and politics.

INCOTERMS/Delivery Terms INCOTERMS are international commercial terms used in shipping documentation that are recognized as the international standard. Ford Motor Company, for example, generally uses standard delivery terms. INCOTERMS are generally letters or abbreviations that represent a universal understanding of the parties involved, terms of sale, point of origin, destination, and party responsible given a certain condition. The purchasing department normally manages this process.

Partnering with Suppliers Ideally, suppliers are treated as partners in satisfying customers. This requires a mature organization with objective information. Communication skills, careful fact gathering, and a good QIS are all needed to achieve this goal. You and your suppliers should keep constant communication open on many fronts to ensure that everything is working well to delight the ultimate customer.



Part I.I

Chapter 9 I. Overcoming Barriers to Quality Improvement

Identify barriers to quality improvement, their causes and impact, and describe methods for overcoming them. (Analyze) Body of Knowledge I.I

A properly implemented total quality management system will have fewer nonconformities, reduced rework and scrap, lower inventory levels, reduced cycle times, greater employee satisfaction, and increased customer satisfaction. These benefits will not occur in many organizations because they are not able to overcome the barriers or obstacles to quality improvement. In a study by Salegna and Fuzel (2000), managers of TQM companies ranked 12 obstacles to implementing quality.

TWELVE OBSTACLES TO IMPLEMENTING QUALITY These barriers or obstacles follow in order of importance.

Lack of Time to Devote to Quality Initiatives Frequently, managers are too busy with their regular activities to take on an additional activity such as quality. Initially, senior management must provide time for employees to devote to the quality initiative. Once a program is well established, the quality activity will become part of the employee’s activities.

Poor Intraorganizational Communication All organizations communicate with their employees in one manner or another. Communications deliver the organizations values, expectations, and directions, provide information about developments, and allow feedback from all levels. The organization must encourage and provide the means for two-way communication so that information flows up as well as down the ladder.

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Lack of Real Employee Empowerment

Part I.I

Too often, empowerment is merely lip service. Individuals should be empowered to make decisions that affect the efficiency of their process or the satisfaction of their customers. Teams need to have the proper training and, at least in the beginning, a facilitator.

Lack of Employee Trust in Senior Management In many organizations, this obstacle will not be a problem because senior management has created an atmosphere of trust in its relationship with the employees. In other organizations, this atmosphere will have to be developed by management being honest with the employees.

Politics and Turf Issues Differences between departments and individuals create problems. The use of multifunctional teams will help to break down long-standing barriers. Restructuring to make the organization more responsive to customer needs may be needed. An example of restructuring is the use of product or customer support teams whose members are permanently reassigned from the areas of quality, production, design, and marketing.

Lack of a Formalized Strategic Plan for Change A formalized plan for change is necessary because individuals resist change— they become accustomed to performing a particular process and it becomes the preferred way. Management must understand and utilize these basic concepts of change: 1. People change when they want to and to meet their own needs 2. Never expect anyone to engage in behavior that serves the organization’s values unless an adequate reason (why) has been given 3. For change to be accepted, people must be moved from a state of fear to trust It is difficult for individuals to change their own behavior, and it is much more difficult for an organization. Honest two-way communication with respectful feedback increases the chances of success.

Lack of Strong Motivation The building of a motivated work force is, for the most part, an indirect process. Management at all levels cannot cause an employee to become motivated; they must create a conducive environment for individuals to become motivated.




Part I.I

View of Quality Program As a Quick Fix Frequently, the quality program is viewed as a quick fix. Quality improvement is a race that does not have a finish. Management must constantly and forever improve the system so that quality and productivity are continually and permanently improved and costs reduced.

Drive for Short-Term Financial Results Too often, organizations focus their efforts on the quarterly financial results. Quality improvement requires an organization to have a strong future orientation and a willingness to make long-term commitments.

Lack of Leadership In order for any organizational effort to succeed, there must be leadership. Leadership requires a substantial commitment in terms of both management time and organizational resources.

Lack of Customer Focus Organizations need to understand the changing needs and expectations of their internal and external customers. Effective feedback mechanisms are necessary for this understanding.

Lack of a Companywide Definition of Quality This obstacle is the least of the twelve and is easy to correct. Experienced quality professionals recommend that all areas of the organization be involved in writing the definition.

SUMMARY OF PART I The quality profession has a human element and a technical element and in Part I we have examined the human element of quality from several different perspectives. First, we briefly reviewed the history of quality and noted the contributions of the leading gurus over the past 80 years or so, starting with Walter Shewhart and highlighting his two greatest successors, W. Edwards Deming and Joseph M. Juran. Some major quality programs discussed were statistical process control, total quality management, lean philosophy, theory of constraints, and Six Sigma. No matter whether one of the above names is used, a successful organization will have some kind of a system for managing its quality. One way to view the quality management system is to look at three parts: strategic planning of the vision and goals, deployment techniques for converting the vision/goals into reality, and an information system to collect, analyze, and report the data.


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Part I.I

Deployment techniques used for selecting and managing projects include return on investment (ROI), PERT, and Gantt charts. Heavy emphasis was also given to performance measurement tools. Next, we discussed professional ethics, including the ASQ Code of Ethics and legal constraints on the quality engineer. Leadership, facilitation, and communication skill are all interrelated. For the organization to achieve its goals in a positive and efficient manner, leaders must translate vision and goals into tangible activities. Executive direction and indirect or “soft” leadership known as facilitation unleashes the energy of middle and lower-level employees. Communication skills are critical to effective leadership and facilitation, as well as to individual career success. The final three chapters addressed the role of quality in dealing with customers, suppliers, and improvement barriers. Two typical techniques are supplier surveys, which tell us what we can expect from our suppliers, and customer surveys, which tell us what our customers think of us. Finally, the section on barriers reinforced the idea that quality improvement is a constant struggle, and the various ideas of this book must be applied again and again in order to maintain momentum toward that elusive but unobtainable goal of perfection.





Part II The Quality System Chapter 10 Chapter 11 Chapter 12

Part II

Chapter 13 Chapter 14 Chapter 15

A. Elements of the Quality System B. Documentation of the Quality System C. Quality Standards and Other Guidelines D. Quality Audits E. Cost of Quality F. Quality Training

A quality system is the enabling mechanism behind the quality assurance and improvement functions of any organization. It is a statement of commitment to quality and tells how quality is to be achieved. The term system implies functional elements, attributes, and relationships. Part I explained quality management systems and quality information systems. We first discuss “the quality system,” including its elements, how it is documented, recognized standards that define or recommend quality systems, and operations that are audited for compliance. Quality is intrinsically related to cost, and we explain the most prominent quality cost systems. Finally, we suggest that training is a quality system, and provide an overview of the role of the quality engineer in quality training.

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Chapter 10

Part II.A

A. Elements of the Quality System

Define, describe, and interpret the basic elements of a quality system, including planning, control, and improvement, from product and process design through quality cost systems, audit programs, and so on. (Evaluate) Body of Knowledge II.A

Every system includes inputs, outputs, activities (processes), and relationships. The elements of a quality system are the activities used to assure customer satisfaction. Typically, these activities depend on the type of organization, its structure, the market, and the particular type of product or service provided. Quality-related activities start with identifying customer needs and extend throughout the lifecycle of the product, as depicted in Figure 10.1. The procedures and work instructions followed within each of these functional areas to achieve the stated quality objectives represent elements of the quality system. It is important to note that the suitability and effectiveness of the system as a whole is determined by the attributes of these individual elements and their relationships. Top management must establish, document, and maintain such systems with overall objectives in mind. System elements closely correspond to the various phases in the traditional product lifecycle depicted in Figure 10.1. In other words, a quality system must cover all the activities that affect product or service quality. ISO 9004:1994 listed the following fourteen functional elements of a quality system. Although ISO 9004:1994 is obsolete, every element is still pertinent, and each of these elements is discussed elsewhere in this book. 1. Quality in marketing. The marketing function is an important source of information regarding the implied and stated needs of the customer, actual field performance, and the degree of customers’ satisfaction with the product. Such information will help identify product problems relative to expectations and initiate corrective measures. Consequently, the marketing function is required to define

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Marketing and research

Product design and development

Disposition or recycling at the end of useful life

Process planning and development

Purchasing

After sales Technical assistance and servicing

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Typical lifecycle phases of a product

Installation and commissioning Verification Sales and distribution Figure 10.1

Part II.A

Production or provision of service

Packaging and storage

Product lifecycle and quality system elements.

Adapted from ANSI/ISO/ASQC Q9004-1-1994. Used with permission.

and document the requirements for a quality product, provide the organization with a formal statement or outline of product requirements, and establish an information feedback system for monitoring field performance on a continuous basis. 2. Quality in specification and design. With the customers’ needs clearly identified, the design function provides the translation of these needs into technical specifications. Formal plans should be prepared and documented for identifying critical stages of the design process and assigning responsibility for each. Design reviews should be conducted at the end of each stage to identify problem areas and initiate corrective actions. All necessary measures should be taken to assure clear and definitive statements of the design requirements. Methods for evaluating conformance during production should also be specified. Design verification and validation through prototype testing or other techniques is required. Provisions should be made for periodic evaluation of the design in light of field performance data. 3. Quality in purchasing. The standard requires that all purchasing activities be planned and controlled by documented procedures. Successful purchase of supplies begins with clear definition of the requirements. A close working relationship with vendors and subcontractors is required to facilitate and secure continuous quality improvements. Procedures must be established for evaluating the capability of the vendors. In some cases the vendor is required to establish a demonstrated capability of meeting design requirements. If incoming inspection is to be performed, the costs involved should be considered and the vendor should be notified of the results.



Part II: The Quality System

Part II.A

4. Quality of processes. This element stipulates the requirements of operation under controlled conditions. The operation of processes and the operating conditions should be specified by documented work instructions. Process capability studies are required to determine the effectiveness of the process and to identify the need for improvements. 5. Control of processes. This is a central element in achieving conformance to design requirements. The type and sensitivity of the control technique depends on the quality characteristic involved or generated, the nature and stability of the process, and its potential capability. Control should extend over the material and parts used, tooling and any shop aids utilized, and environmental conditions. Proper identification of materials from the time of receipt to product delivery and installation is required. Statistical techniques for monitoring process variables are described in Chapter 37. 6. Product verification. This element addresses the allocation of test and inspection points in the process for the purpose of verifying conformance. Verification of incoming materials and products at various stages of the process prevents the unnecessary cost of further processing nonconforming units. Final product verification is performed to prevent shipping nonconforming units to customers. 7. Control of inspection, measuring, and test equipment. All measuring systems used in the development, production, and installation of products should be controlled. Documented procedures should be established to maintain the measuring process in a state of statistical control. The procedure includes initial calibration against a reference standard as well as periodic recall for adjustment and recalibration, and may be extended to all vendors. 8. Control of nonconforming product. Documented procedures for dealing with nonconforming units should be established and maintained. These procedures include steps for the identification, segregation, and review of the nonconformities. The objective is to avoid the unintended use of such units and the consequent dissatisfaction of internal and external customers. 9. Corrective action. A quality system should define the responsibility and authority for instituting corrective actions. These actions should be planned after identifying the root causes of the problem. Actions to eliminate these causes may involve a variety of functions such as design, purchasing, production, and quality control. The objective should be to prevent the recurrence of these causes and improve quality. Corrective action is required to monitor the effect of such actions in order to ensure that the above objectives are met. 10. Postproduction activities. Included here are procedures for product storage, delivery, and installation activities. These activities should prevent deterioration of product quality, secure proper identification, and safeguard against improper installation. Also, the quality system should allow for a feedback of information regarding field performance, customer satisfaction, and the initiation of corrective actions.


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11. Quality records. These are records indicating the results of implementing the system and providing subjective means for evaluating its effectiveness. An organization is required to establish and maintain documented procedures for identification, collection, storage, retrieval, and disposition of these records. Analysis of the quality records can help identify trends in quality performance, as well as the need for and effectiveness of corrective actions. In addition, records should indicate authorized changes to the quality manual and any modifications made in the procedures or work instructions. Documentation systems are discussed in the next chapter.

Part II.A

12. Personnel. Employee training, qualification, and motivation are key factors in developing the human resources of an organization and emphasizing quality awareness among them. Documented procedures for identifying and providing training programs at all levels should be established and maintained. Periodic assessment of personnel skills and capabilities should be considered. Recognition of proper job performance and the use of motivational programs are ways in which management can support quality improvement efforts. 13. Product safety. Procedures are needed for identifying the safety aspects of products and processes. These aspects are best identified and considered during the design phase of the product lifecycle. Further, the rule of strict liability has created a need to plan for field failures and their legal implications. These procedures may include documenting prototype and product design evaluation testing for safety, providing adequate operational instructions with warnings against known hazards, and developing contingency plans for product recall. Failure mode and effects analysis, fault tree analysis, and hazard function analysis are all valuable tools. 14. Use of statistical methods. This element of the quality system is concerned with the analytical techniques used to measure, control, and improve quality throughout the product lifecycle. These include design of experiments, estimation and test of significance, control charts, and sampling inspection.



Chapter 11

Part II.B

B. Documentation of the Quality System

Identify and apply quality system documentation components, including quality policies, procedures to support the system, configuration management and document control to manage work instructions, quality records, and so on. (Apply) Body of Knowledge II.B

Plans for achieving customer satisfaction and assuring that the quality of products or services are documented in a quality manual are sometimes referred to as the quality program, which represents the first of two major system efforts: documentation and implementation. Compliance, accuracy, and clarity are critical characteristics of documentation. A generic quality manual may be viewed as a composite document of four tiers—also known as the documentation hierarchy or pyramid—as illustrated in Figure 11.1. Starting from the top, these tiers are policies, procedures, instructions, and records. The first tier represents a policy statement, which explains what the company stands for and what its commitments are. This is the opening statement of the quality manual, indicating the management policy and objectives for quality. There is usually a policy statement for each of the requirements of the applicable standard. The second tier is a procedures tier, which provides an overview of how a company does its business. Direct, yet simple statements indicate who is responsible for what in achieving the requirements. In some cases the procedures will be in the quality manual but more often they will be distributed, often online. The third tier represents work instructions, which spell out the how-to in a clear manner. An organization may choose to include detailed work instructions or exclude proprietary information. Work instructions are documented in many ways, depending on the function at hand. Format examples are in ISO/TR 10013:2001.

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Document contents Layer I Statements of the quality policy and objectives Policy Procedure

Work instructions

Figure 11.1

Layer III Detailed work documents Layer IV Results of implementing the quality system

Part II.B

Quality records

Layer II Description of the activities needed to implement the system

Tiers of the quality documentation hierarchy.

Finally, the fourth tier shows the results obtained by implementing the quality system. These results are documented and maintained to form quality records. These records provide subjective evidence that the system is being implemented and is effective. Records must be maintained for a specified time in a protected format retrievable for analysis. A quality manual need not be partitioned into four separate parts to include the four tiers—this is only a model. Most likely, all the elements will not even be included in one document. The structure of the manual is best selected based on the nature of the organization and the applicable standard. In a small organization a separation between the work instructions and the procedures manuals may not be necessary, as it would be for a large organization. However, if the tiers are separated, it is important to provide cross-references or links between these tiers to assure effective documentation. Upon the completion of the quality manual, a final review to determine its competence, accuracy, and clarity is undertaken. Top management should endorse the contents of the reviewed copy and authorize its release. Authorized copies of the manual should be distributed in total or by section to intended users throughout the organization. Proper distribution and control can be aided, for example, by a dedicated document control function. Parsowith (1995) identifies the following four requirements for proper document control: 1. A process is in place for the generation of documents that includes the writing of the policies and procedures drawings and specifications or other required documentation, approval of the contents of the documents, and the distribution of the documents.




2. Documentation fulfilling the needs of contractual or process requirements is available at all locations in which these functions are performed. 3. A process is in place for the control of revisions to or redistribution of documents using the same system as the original document distribution.

Part II.B

4. A process is in place for the identification and removal of obsolete documents to ensure against unintended use.



Chapter 12 C. Quality Standards and Other Guidelines

Part II.C

Define and distinguish between national and international standards and other requirements and guidelines, including the Malcolm Baldrige National Quality Award (MBNQA), and describe key points of the ISO 9000 series of standards and how they are used. [Note: Industry-specific standards will not be tested.] (Apply) Body of Knowledge II.C

NATIONAL VERSUS INTERNATIONAL STANDARDS The concept of national standards—which spelled out requirements for how things were to be done in the production of goods and services—grew hand in hand with the industrialization of national economies. One of the first national standards was a boiler standard, issued late in the 19th century by the American Society of Mechanical Engineers. Over time, different nations adopted standards that, while entirely appropriate to their own national needs, were in conflict with each other, which meant that a company would either have to customize its operations to different national standards, which could become very inefficient and expensive, or to forfeit the opportunity to do business in some countries. These conflicts between different national standards naturally gave rise to the concept of international standards. Around the beginning of the 20th century, when electricity became a powerful force in many different countries, an international standards organization, the International Electrotechnical Institute, was established. This organization provided precedence for the later establishment of the International Organisation for Standardization, familiarly known as ISO. In the 1970s and 1980s, as global commerce became a matter of interest to more than 100 different countries and thousands of different companies, a major cooperative effort of international quality professionals led to the ISO 9000 family of standards.

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THE ISO 9000 FAMILY ISO 9000:2005 refers to both a family of three related standards and to one of the standards in that family. The purpose of this family is “to assist organizations, of all types and sizes, to implement and operate effective quality management systems” and consists of three standards: quality management vocabulary, requirements, and guidelines for performance.

Part II.C

• ISO 9000: Quality management systems—Fundamentals and vocabulary, provides the fundamentals and terminology of quality management systems. • ISO 9001: Quality management systems—Requirements, specifies the needed requirements for an organization to provide products that aim to enhance customer satisfaction. • ISO 9004: Quality management systems—Guidelines for performance improvements, suggests ways to improve organizational performance and customer satisfaction beyond the requirements of ISO 9001. Within the ISO 9000 family, the following eight quality management principles are consistently emphasized: 1. Customer focus. Understand the customer’s needs, meet their requirements, and strive to exceed their expectations. 2. Leadership. Establish unity of purpose and direction. 3. Involvement of people. Help people at all levels to have a sense of ownership and involvement. 4. Process approach. Strive to manage activities and related resources as a process. 5. Systems approach to management. Identify the interrelated processes and how they affect each other. 6. Continual improvement. Maintain the ideal of continually improving all aspects of the organization. 7. Factual approach to decision making. Use data and information to guide decisions. 8. Mutually beneficial supplier relationships. Recognize that suppliers and customers are dependent on each other and that a mutually beneficial relationship helps both to add value. ISO 9000:2005 replaced ISO 9000:2000, which replaced ISO 9000:1994. The major changes from ISO 9000:1994 to ISO 9000:2000 are summarized by Cianfrani, Tsiakals, and West in The ASQ ISO 9000:2000 Handbook (2002). The details and implications of all the changes in the ISO 9000:2000 family are covered in Chapter 8 in The ASQ ISO 9000:2000 Handbook. • Greater focus on the customer and customer satisfaction (clause 8.2.1)


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• A new focus on the process approach (clauses 0.2, 4.1, 5.4.2, 7.1, and 8.1) • Clarification of requirements for continual improvements (for example, clause 8.5) • Greater emphasis on the role of top management (clause 5) • Measurable quality objectives (clause 5.4.1) • New requirements for data collection and analysis (clause 8.4) • Reduced emphasis on documented procedures (clause 4.2)

• Consistency between ISO 9001 and ISO 9004 (clause 0.3) • Compatibility with ISO 14000 (clause 0.4)

Part II.C

• Shift in emphasis from training to providing competent people (clause 6.2)

• Elimination of ISO 9002 and ISO 9003 (clause 1.2) • Modification of the purpose of internal audits (clause 8.2.2) The following sections are overviews of the three standards that make up the ISO 9000:2000 family.

ISO 9000:2005 Fundamentals and Vocabulary This document is the language foundation for the entire worldwide system of development, implementation, auditing, and registration of ISO 9001:2008. All definitions of terms are based on many person-years of research and consultation. In all, 85 concepts and their associated terms are defined in the following 10 categories: quality, management, organization, process and product, characteristics, conformity, document, examination, audit, and measurement process. The standard differentiates between a concept and a term as follows: a concept is a unit of knowledge created by a unique combination of characteristics. A term is the verbal designation of the concept as it applies to a specific field of study. Two criteria apply to all of the ISO 9000 terms and definitions: 1. Avoid technical language in technical descriptions. 2. Employ a coherent and harmonized vocabulary that is understood by all actual and potential users. Because the standards are translated into many different languages, it is critical that all users have the same understanding of what the terms mean. This goal has been accomplished by having representatives from more than 100 different countries intimately involved in polishing the definitions. A good example of the need for this kind of language-specific polishing is the term interested party. In English, a more common term would be stakeholder. However, in some languages, a literal translation of stakeholder is someone holding a stick. Many such conflicts and ambiguities have been resolved over the years.




ISO 9001:2008 Requirements

Part II.C

This document is the set of requirements that organizations must satisfy in order to achieve ISO 9001 registration. Such registration is required in some industries and very highly regarded in many others. Over 140 countries have ISO 9001 registration programs. Many people inadvertently refer to this key document as “ISO 9000:2008.” (Remember: ISO 9000 refers to a family of three documents, and also to the Vocabulary document; ISO 9001 refers to the requirements document.) The standard has eight major parts, or clauses. The first three are general clauses: 1. Scope. This tells what organization(s), location(s), process(es), product(s), and so on, are covered. 2. Normative references. These cite other standards which, by being listed, constitute provisions of the ISO 9001 standard. 3. Terms and definitions. Here, reference is made to ISO 9000, which contains all definitions applicable to ISO 9001. The remaining five are technical clauses and are listed here as they appear in the standard. For further details, consult the standards themselves. The five technical clauses (clauses 4–8) are: 4. Quality management system 4.1 General requirements 4.2 Documentation requirements 5. Management responsibility 5.1 Management commitment 5.2 Customer focus 5.3 Quality policy 5.4 Planning 5.5 Responsibility, authority and communication 5.6 Management review 6. Resource management 6.1 Provision of resources 6.2 Human resources 6.3 Infrastructure 6.4. Work environment 7. Product realization 7.1 Planning of product realization



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7.2 Customer-related processes 7.3 Design and development 7.4 Purchasing 7.5 Production and service provision 7.6 Control of monitoring and measuring devices 8. Measurement, analysis and improvement 8.1 General 8.2 Monitoring and measurement

8.4 Analysis of data 8.5 Improvement

Part II.C

8.3 Control of nonconforming product

ISO 9004:2000 Guidelines for Performance Improvements Whereas ISO 9001 is compliance-based, ISO 9004 is improvement-based. All the great ideas in ISO 9004 are guidelines, not requirements. There is some controversy in the field of quality on the usefulness of ISO 9004 and it has not been widely adopted. The developers of the ISO 9000 family made ISO 9001 and ISO 9004 completely compatible in structure, so it is easy to follow any of the following three paths: Path A. You have no present wish to be registered to ISO 9001 requirements but you want to install a powerful quality management system now, with the option to go for ISO 9001 registration later. So you build your present quality system on the guidelines of ISO 9004. Nothing you do in following the ISO 9004 guidelines will cause you trouble if you later seek ISO 9001 registration. Path B. You are presently registered to ISO 9001 but want to upgrade to a more powerful system that leads to performance improvement. Without making any changes in your present quality management system, you can start to selectively apply the ISO 9004 guidelines. Path C. You are not registered but want to become registered and also want to put quality improvement procedures in place that are not required for registration. You can work with ISO 9001 and ISO 9004 simultaneously, devoting to each the resources you deem appropriate to achieving your goals. ISO 9004 uses exactly the same format, definitions, and clauses as ISO 9001. Like ISO 9001 it relies on the eight principles of quality management mentioned earlier. However, 9004 is almost twice as long as 9001. Whereas compliance to 9001 raises the issue of corrective action, the 9004 guidelines suggest how and where to go further in improving performance. ISO 9004 contains two annexes (not present in 9001) in the form of guides for two differing approaches to improvement. The first annex is a set of guidelines




for self-assessment. The second lays out a specific process for self-improvement. Either approach can work. Westcott (2003) expands on both approaches, helps management develop plans to implement ISO 9004, and provides some case studies of successful implementation. The best place to start, of course, is with the ISO 9004 standard itself.

Part II.C

SOME OTHER QUALITY STANDARDS There exist a number of industry-specific standards that may have an impact on the quality engineer. The automobile, telecommunications, and biomedical industries have all created industry-specific standards that emulate ISO 9001 but impose additional requirements. The American automobile industry took the lead in creating its own industry standard with QS 9000. Later, Americans and Europeans joined forces to create the ISO/TS 16949 standard, which is mandatory for many original equipment manufacturers (OEMs). The telecommunications quality standard is TL 9000 and the biomedical quality standard is ISO 13485:2003. These standards as well as other pertinent publications are available from ASQ Quality Press by phoning 800–248–1946 or visiting the ASQ Web site at www.asq.org/quality-press. Commentary and offers of assistance on all three of these standards can be found by entering the standard number into a Web browser search engine.

MALCOLM BALDRIGE NATIONAL QUALITY AWARD The Malcolm Baldrige National Quality Award (MBNQA), another quality management approach, emphasizes results rather than procedures or requirements. Congress established this award in 1987 to recognize U.S. organizations for their achievements in quality and business performance and to raise awareness about the importance of quality and performance excellence as a competitive edge. The award is named in honor of Malcolm Baldrige, who was secretary of commerce at the time of his death in 1987. The award is not given for specific products or services. Awards may be given annually in each of these categories: manufacturing, service, small business, education, healthcare, nonprofit, and government. While the Baldrige Award and the Baldrige recipients comprise the very visible centerpiece of the U.S. quality movement, a broader national quality program has evolved around the award and its criteria. A report, Building on Baldrige: American Quality for the 21st Century, by the private Council on Competitiveness, said, “More than any other program, the Baldrige Quality Award is responsible for making quality a national priority and disseminating best practices across the United States.” The U.S. Commerce Department’s National Institute of Standards and Technology (NIST) manages the Baldrige National Quality Award Program in close cooperation with the private sector. Since its inception, several million copies of the award criteria have been distributed and there have been over 60 recipients of the award.



99

The Baldrige Award Criteria The Malcolm Baldrige National Quality Award is awarded according to these criteria for performance excellence: 1. Leadership. Examines how senior executives guide the organization and how the organization addresses its responsibilities to the public and practices good citizenship. 2. Strategic planning. Examines how the organization sets strategic directions and how it determines key action plans.

4. Information and analysis. Examines the management, effective use, and analysis of data and information to support key organization processes and the organization’s performance management system.

Part II.C

3. Customer and market focus. Examines how the organization determines requirements and expectations of customers and markets.

5. Human resource focus. Examines how the organization enables its workforce to develop its full potential and how the workforce is aligned with the organization’s objectives. 6. Process management. Examines aspects of how key production/delivery and support processes are designed, managed, and improved. 7. Business results. Examines the organization’s performance and improvement in its key business areas: customer satisfaction, financial and marketplace performance, human resources, supplier and partner performance, and operational performance. This category also examines how the organization performs relative to competitors. Further information about the Malcolm Baldrige National Quality Award, including procedures for ordering the criteria, is available at the following Web site: www.quality.nist.gov.



Chapter 13

Part II.D.1

D. Quality Audits

A

quality system audit, as defined by ANSI/ASQC A3-1987, is a systematic and independent evaluation of the quality system and its execution, looking at both design and performance of the system. It is a fact-finding process that compares actual results with specified standards and plans. It provides feedback for improvement. It differs from inspection, which emphasizes acceptance or rejection, and surveillance, which is ongoing continuous monitoring.

1. TYPES OF AUDITS

Describe and distinguish between various types of quality audits such as product, process, management (system), registration (certification), compliance (regulatory), first, second, and third party, and so on. (Apply) Body of Knowledge II.D.1

Quality audits may be classified according to the party conducting them, their scope, and the audit method used. In general, three parties are involved in an audit: (1) the organization requesting the audit, or client, (2) the party conducting the audit, or the auditor, and (3) the organization to be audited, or the auditee. When the auditor is an employee of the organization being audited (auditee), the audit is classified as an internal quality audit. For the purposes of maintaining objectivity and minimizing bias, internal auditors must be independent from the activity being audited. On the other hand, when the auditors are employees of the client or an independent organization or third party hired for the purpose, the audit is termed an external quality audit. In this case, the auditors are clearly independent of the auditee and are in a position to provide the client with an unbiased, objective assessment. This is the type of audit required to permit listing in a register or to meet mandatory quality requirements. However, the time required and costs involved in an external audit are much higher as compared to internal audits. 100


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Part II.D.2

Another way to classify quality audits is by scope and extent. An audit may be as comprehensive as needed or requested by the client. The most comprehensive type of audit is the quality system audit, which examines suitability and effectiveness of the system as a whole. This involves both the documentation and implementation aspects of the quality system. Reasons for initiating a system audit may range from evaluating a potential supplier to verifying an organization’s own system. Audits of specific elements of a system, processes, products, or services, are also possible. These are limited in scope and are typically referred to using a modifier preceding the term quality audit. Examples include process quality audits and product quality audits. The method by which the quality audit is conducted provides yet another way to classify. Audits may be conducted by location or function. A location-oriented audit provides an in-depth examination of all the quality-related activities within a given location. In a function-oriented audit, an activity is examined in all the locations where the activity is carried out. It is important to note that these classifications are not mutually exclusive and, in practice, cross-classifications of a quality audit are possible. The following four purposes of quality audits are listed in ANSI/ISO/ASQ QE19011S-2008: 1. To meet requirements for certification to a management system standard 2. To verify conformance with contractual requirements 3. To obtain and maintain confidence in the capability of a supplier 4. To contribute to the improvement of the management system

2. ROLES AND RESPONSIBILITIES IN AUDITS

Identify and define roles and responsibilities for audit participants such as audit team (leader and members), client, auditee, and so on. (Understand) Body of Knowledge II.D.2

Each of the three parties involved in an audit—the client, the auditor, and the auditee—plays a role that contributes to its success. The client, the party that initiates the audit, selects the auditor and determines the reference standard to be used. The client, typically the end user of the audit results, determines the type of audit needed (system, process, product, and so on) as well as its time and duration. The selected auditor, whether an individual or a group, needs to adhere to the role of a third party. That is, the auditor must maintain objectivity and avoid bias in conducting the audit. The auditor must comply with any confidentiality requirements mandated by the auditee. An experienced individual is appointed


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as lead auditor to communicate audit requirements, manage the auditing activities, and report the results. For rules, qualifications, and evaluation criteria for an auditor, see ANSI/ISO/ASQ QE19011S-2008. Finally, the auditee has the responsibility of accommodating the audit, which entails providing the auditors access to the facilities involved and copies of all relevant documentation. The auditee is also expected to provide the resources needed and select staff members to accompany the auditors.

Part II.D.3

3. AUDIT PLANNING AND IMPLEMENTATION Describe and apply the steps of a quality audit, from the audit planning stage through conducting the audit, from the perspective of an audit team member. (Apply) Body of Knowledge II.D.3

Proper planning is a key factor in achieving an efficient quality audit. Planning should be conducted with consideration of the client expectations. This includes the scope, depth, and time frame. The lead auditor has the responsibility of planning and conducting the audit and should be authorized to perform these activities. Planning an audit, just like any other activity, should address the questions of what, when, how, and who. That is, what elements of the quality system are to be audited? Against what document or reference standard? The answers to both questions are determined by the client and should be communicated clearly to the auditee. When to start and when to conclude the audit? A schedule of the audit activities needs to be prepared and communicated to both the client and the auditee. It is the lead auditor’s responsibility to inform the client of any delays, report their reasons, and update the completion date of the audit. The method of conducting the audit also should be addressed. Working documents need to be prepared, including checklists of the elements to examine, questions to ask, and activities to monitor. A number of references provide generic checklists that can be used as templates. However, it is best to design a checklist to suit the audit at hand and its specific scope and objectives. Forms for collecting auditors’ observations and the supporting evidence also should be included in the working document. Working documents typically are reviewed by an experienced auditor and approved by the lead auditor before implementation. It is recommended that the auditor explain the methods planned to the auditee. This should help the organization better prepare for the audit and ease the fear usually attached to the process. The question of who will examine specific elements, processes, or products addresses the qualifications and experiences of the individual auditors (assessors) needed. With the client expectations in mind, the lead auditor should assign the various tasks among his or her team.


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Part II.D.4

An audit is usually conducted in three steps. (1) A pre-examination or opening meeting with the auditee marks the beginning of the process. During this meeting, the lead auditor introduces team members to the senior management of the auditee and explains the objectives of the audit and the methods used. The auditee is represented by selected members of the organization who facilitate and assist in the process and submit a documented description of the quality system or element to be examined. Issues regarding proprietary information typically are addressed and resolved before starting the audit. The next step (2) involves a suitability audit of the documented procedures against the selected reference standard. Observed nonconformities at this stage of the audit should be reported to both the client and the auditee for immediate action. The auditing process should pause to allow for corrective measures. For the third step (3), the auditor examines in depth the implementation of the quality system. The auditor maintains records of all nonconformities observed and the supporting data. Provisions should be made in the audit plan to allow additional investigation of clues suggesting nonconformities revealed by the data collected. The auditee management should be made aware of, and acknowledge, all the nonconformities observed during the audit. This step concludes with a closing meeting with the auditee’s management for a presentation of findings. In some cases, the auditor may be required to recommend corrective measures for improving the system. However, it is up to the auditee to plan and implement these measures in a way that best suits the organization.

4. AUDIT REPORTING AND FOLLOW-UP Identify, describe, and apply the steps of audit reporting and follow up, including the need to verify corrective action. (Apply) Body of Knowledge II.D.4

A final report is submitted to the client indicating the facts of the audit and conclusions regarding the ability of the subject system, element, process, or product to achieve quality objectives. Proper planning and execution of the audit facilitates the preparation of this report and provides data to support its conclusions. The lead auditor is responsible for the accuracy of the report and the validity of its conclusions. The report should be submitted to the client, who in turn is responsible for providing a copy to the auditee. The audit final report should include, at a minimum, the following: 1. Type of audit conducted 2. Objectives of audit 3. Identification of involved parties: auditor, auditee, and third party



4. Audit team members 5. Critical nonconformities and other observations 6. Audit standards and reference documents used 7. Determination of proper corrective action(s) 8. Duration of audit 9. Audit report distribution and date

Part II.D.4

10. Audit results and recommendations 11. Audit-related records Should the auditee initiate improvement efforts to correct nonconformities, the three parties should agree on a follow-up audit to verify the results. The plan, audit, report, and improve cycle may be repeated whenever systems and/or requirements change. The results attained provide a measure of the effectiveness of the audit. Improvement efforts also should be directed to identifying and eliminating the root causes of reported nonconformities and identifying the corrective action(s) to be taken. Root causes represent the main reason behind the occurrence of a nonconformance or an undesirable condition or status. These corrective actions may then be validated by performing tests, inspections, or even more audits.



Chapter 14 E. Cost of Quality

Part II.E

Identify and apply COQ concepts, including cost categories, data collection methods and classification, and reporting and interpreting results. (Analyze) Body of Knowledge II.E

To achieve the most effective improvement efforts, management should ensure that the organization has ingrained in its operating principles the understanding that quality, speed, and cost are complementary—and not conflicting—objectives. Traditionally, recommendations made to management were choices between quality, speed, and cost, where they could pick two of these but not all three at once. Experience throughout the world has shown, and management is beginning to see, that this is not true. Good quality leads to increased productivity and reduced quality costs, and eventually to increased sales, market penetration, and profits. The purpose of cost of quality (COQ) techniques is to provide a tool to management for facilitating quality program and quality improvement activities. Quality cost reports can be used to point out the strengths and weaknesses of a quality system. Improvement teams can use COQ reports to describe the monetary benefits and ramifications of proposed changes. Return-on-investment (ROI) models and other financial analyses can be constructed directly from quality cost data to justify proposals to management. Improvement team members can use this information to rank problems in order of priority. In practice, quality costs can define the activities of quality program and quality improvement efforts in a language that management can understand and act on—dollars. Any reduction in quality costs will have a direct impact on gross profit margins and can be counted immediately as pretax profit.

THE ECONOMICS OF QUALITY The expression the economics of quality has contributed to some confusion surrounding the true business and economic value of quality management. Some people believe there is no economics of quality, that is, it is never economical to 105



Part II.E

106 Part II: The Quality System

ignore quality. At the other extreme are those managers who believe it is uneconomical to have 100 percent quality. These managers feel free to make arbitrary decisions about the needed quality of a product or service, usually expressed by the term “that’s good enough. “ The facts about quality management and quality costs, however, show that the real value of a quality program is determined by its ability to contribute to customer satisfaction and profits. Quality cost techniques provide tools for management in its pursuit of customer satisfaction, quality improvement, and profit contributions. Whether for manufacturing or service, a quality cost program will lend credence to the business value of the quality management program and provide cost justification for the corrective actions demanded. Quality cost measurements provide guidance to the quality management program much as the cost accounting system does for general management. Quality cost measurements define and quantify those costs that are directly affected, both positively and negatively, by the quality management program, thus allowing quality to be managed more effectively. Simply stated, quality costs are a measure of the costs specifically associated with the achievement or nonachievement of product or service quality—including all product or service requirements established by the company and its contracts with customers and society. More specifically, quality costs are the total of the costs incurred by: (a) investing in the prevention of nonconformances to requirements (prevention costs), (b) appraising a product or service for conformance to requirements (appraisal costs), and (c) failure to meet requirements (failure costs). Quality costs represent the difference between the actual cost of a product or service and what the reduced cost would be if there were no possibility of substandard service, failure of products, or defects in their manufacture. Every company lives with significant costs that fit this description. Unfortunately, significant chunks of quality cost are normally overlooked or unrecognized simply because most accounting systems are not designed to identify them. As this is generally the case, it is not too difficult to understand why top management of most companies is more sensitive to overall cost and schedule than to quality. The interrelationship of quality, schedule, and cost, without attention to the contrary, is likely to be unbalanced in favor of schedule and cost—and often unwittingly at the expense of quality. This imbalance will continue to exist as long as the real cost of quality remains hidden among total costs. In fact, such a condition can easily set the stage for a still greater imbalance whenever the rising, but hidden, true cost of quality grows to a magnitude that can significantly affect a company’s competitive position. When the cost of quality rises without constraint, or is tolerated at too high a level, failure to expose the condition is a sign of ineffective management. Yet, it is entirely possible for this condition to exist without top management’s awareness. A quality cost program can provide specific warnings against oncoming, dangerous, quality-related financial situations. An argument for needed quality improvement is always weak when it must deal in generalities and opinions but it will become unmistakably clear when a company suddenly finds itself in serious, expensive quality trouble.


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On the premise that any dollar expenditure that could have been avoided will have a direct negative effect on profits, the value of clearly identifying the cost of quality should be obvious. Achieving this clarity of identification, however, is more easily said than done. A real danger lies in finding and collecting only a small portion of the costs involved and assuming it represents the total. There are as many ways of hiding costs in industry as there are people with imagination. This is an all too natural phenomenon in organizations that are never fully charged with all inefficiencies—because some inefficiencies are hidden and not measured—and thus are able to maintain an illusion of effective management.

GOAL OF A QUALITY COST SYSTEM Part II.E

The goal of any quality cost system is to facilitate quality improvement efforts that will lead to operating cost reduction opportunities. The strategy for using quality costs is quite simple: (1) take direct attack on failure costs in an attempt to drive them to zero, (2) invest in the right prevention activities to bring about improvement, (3) reduce appraisal costs according to results achieved, and (4) continuously evaluate and redirect prevention efforts to gain further improvement. This strategy is based on the premise that: • For each failure there is a root cause • Causes are preventable • Prevention is always cheaper In a practical sense, real quality costs can be measured and then reduced through the proper analysis of cause and effect. As failures are revealed through appraisal actions or customer complaints, they are examined for root causes and eliminated through corrective action. The further along in the operating process that a failure is discovered, that is, the nearer to product or service use by the customer, the more expensive it is to correct. Usually as failure costs are reduced, appraisal efforts also can be reduced in a statistically sound manner. The knowledge gained from this improvement can then be applied, through prevention activities or disciplines, to all new work. By minimizing quality costs, quality performance levels can be improved.

MANAGEMENT OF QUALITY COSTS Managing quality costs begins with a general understanding and belief that improving quality performance and improving quality costs are synonymous (the economics of quality). The next step is recognizing that measurable quality improvement also can have a tangible effect on other business measures, such as sales and market share. The proviso, however, is that quality costs must be measured and must reflect cost or lost opportunities to the company. It should be further understood that the cost of quality is a comprehensive system, not a piecemeal tool. There is a danger in responding to a customer problem only with added internal operations, such as inspections or tests. For service operations, this could mean more operators. While this may solve the immediate



Part II.E


customer problem, the added costs may, in fact, destroy profit potential. A comprehensive quality management program will force the analysis of all associated quality costs, making these added internal costs appear clearly as just one step toward the ultimate resolution—prevention of the root cause of the problem. Quality costs should, therefore, become an integral part of any quality management program and, in turn, any quality system or quality improvement activity. Overall quality cost data will point out the potential for improvement and provide management with the basis for measuring the improvement accomplished. Total quality costs are intended to represent the difference between the actual cost of a product or service and what the cost would be if quality was perfect. It is, according to Dr. Joseph Juran, “gold in the mine, “ waiting to be extracted. When you zero in on the elimination of failure costs and then challenge the level of appraisal costs, you will not only be managing the cost of quality, you will be mining gold.

QUALITY COST CATEGORIES To manage quality costs they must be categorized. The three major categories commonly used are prevention costs, appraisal costs, and failure costs. See Table 14.1 for a list of quality cost elements by category. Prevention costs are the costs of all activities specifically designed to prevent poor quality in products or services. Examples are the costs of quality planning, training programs, and quality improvement projects. Appraisal costs are the costs associated with measuring, evaluating, or auditing products or services to assure conformance to quality standards and performance requirements. These include the costs of inspection, testing, product or service audits, process audits, and calibration of measuring and test equipment. Failure costs are those costs resulting from products or services not conforming to requirements or customer needs. They are usually divided into two types, internal and external. 1. Internal failure costs occur prior to delivery or shipment of the product or furnishing of a service to the customer, such as the costs of scrap, rework, material review, and so on. 2. External failure costs occur after delivery of the product and during or after furnishing of a service to the customer. Examples include the costs of processing customer complaints, customer returns, warranty claims, and product recalls. Total quality cost is the sum of these costs—prevention, appraisal, and failure— and represents the difference between the actual cost of a product or service and what the reduced cost would be if there were no possibility of substandard service, failure of products, or defects in their manufacture.

IMPLEMENTATION To implement a quality cost program, the need for the program must first be determined. This need should be presented to management in a way that will justify


1.4.3 1.4.4 1.4.5 1.5 1.5.1 1.5.2 1.5.3 1.5.4 1.5.5 1.5.6 1.5.7 1.6

1.0 1.1 1.1.1 1.1.2 1.1.3 1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.4 1.4.l 1.4.2 1.4.2.1

Prevention Costs Marketing/Customer/User Marketing Research Customer/User Perception Surveys/Clinics Contract/Document Review Product/Service/Design Development Design Quality Progress Reviews Design Support Activities Product Design Qualification Test Service Design Qualification Field Trials Purchasing Prevention Costs Supplier Reviews Supplier Rating Purchase Order Tech Data Reviews Supplier Quality Planning Operations (Manufacturing or Service) Prevention Costs Operations Process Validation Operations Quality Planning Design and Development of Quality Measurement and Control Equipment Operations Support Quality Planning Operator Quality Education Operator SPC/Process Control Quality Administration Administrative Salaries Administrative Expenses Quality Program Planning Quality Performance Reporting Quality Education Quality Improvement Quality System Audits Other Prevention Costs

Quality cost elements by category. 2.0 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.2 2.2.1 2.2.1.1 2.2.1.2 2.2.1.3 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6 2.2.6.1 2.2.6.2 2.2.6.3 2.2.7 2.3 2.3.1 2.3.2 2.3.3 2.4 2.5 Appraisal Costs Purchasing Appraisal Costs Receiving or Incoming Inspections and Tests Measurement Equipment Qualification of Supplier Product Source Inspection and Control Programs Operations (Manufacturing or Service) Appraisal Costs Planned Operations Inspections, Tests, Audits Checking Labor Product or Service Quality Audits Inspection and Test Materials Set-Up Inspections and Tests Special Tests (Manufacturing) Process Control Measurements Laboratory Support Measurement (Inspection and Test) Equipment Depreciation Allowances Measurement Equipment Expenses Maintenance and Calibration Labor Outside Endorsements and Certifications External Appraisal Costs Field Performance Evaluation Special Product Evaluations Evaluation of Field Stock and Spare Parts Review of Test and Inspection Data Miscellaneous Quality Evaluations

Part II.E

Table 14.1

Continued



Internal Failure Costs Product/Service Design Failure Costs (Internal) Design Corrective Action Rework Due to Design Changes Scrap Due to Design Changes Production Liaison Costs Purchasing Failure Costs Purchased Material Reject Disposition Costs Purchased Material Replacement Costs Supplier Corrective Action Rework of Supplier Rejects Uncontrolled Material Losses Operations (Product or Service) Failure Costs Material Review and Corrective Action Costs Disposition Costs Troubleshooting or Failure Analysis Costs (Operations) Investigation Support Costs Operations Corrective Action Operations Rework and Repair Costs Rework Repair Reinspection/Retest Costs Extra Operations Scrap Costs (Operations) Downgraded End-Product or Service Internal Failure Labor Losses Other Internal Failure Costs

Quality cost elements by category. (Continued) 4.0 4.1 4.2 4.3 4.3.1 4.4 4.5 4.6 4.7 4.8 4.9 External Failure Costs Complaint Investigations/Customer or User Service Returned Goods Retrofit Costs Recall Costs Warranty Claims Liability Costs Penalties Customer/User Goodwill Lost Sales Other External Failure Costs

Source: ASQ Quality Costs Committee. Principles of Quality Costs: Principles, Implementation, and Use. 3rd ed. Ed. Jack Campanella. Milwaukee: ASQ Quality Press, 1999.

3.0 3.1 3.1.1 3.1.2 3.1.3 3.1.4 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.2.5 3.3 3.3.1 3.3.1.1 3.3.1.2 3.3.1.3 3.3.1.4 3.3.2 3.3.2.1 3.3.2.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7 3.4

Table 14.1

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Part II.E

the effort and interest them in participating. To interest management, the need must be justified. One way to do this is by establishing a trial program. It can be simple. For this purpose, only major costs need to be gathered and only readily available data need be included. Much of the required data may be available already. If necessary, some of these costs may even be estimated. When setting up the trial program, there is no need to do everything—there’s time for that later. Select a program, facility, or area of particular interest to management. The results should be sufficient to sell them on the need for the program. Most trial runs will show eye-opening results, spectacular enough to make management sit up and take notice. They will see quality costs running as much as 20 percent or more of sales dollars (according to some studies), and opportunities for significant savings will be obvious. With top management sold on the program, getting the much-needed cooperation of the accounting people should be easy. With management sold and with accounting ready to go, the specific quality costs to be collected must be determined. To do this, tasks must be classified as to prevention, appraisal, or failure, and listed together with the departments responsible for them. Remember that quality costs are not only incurred by the quality department. To determine the prevention costs in the effort to prevent poor quality, such tasks performed in the company should be listed together with the departments responsible for those tasks. In a like manner, appraisal cost elements are determined by listing those tasks associated with the inspection or test of products or services for the detection of poor quality. For failure costs, determine those costs that would not have been expended if quality were perfect. If quality were perfect there would not be any rework, customer complaints needing response, or need for corrective action. Remember to divide failure costs into internal and external categories. Quality cost elements may differ from company to company and particularly from industry to industry. However, the overall categories of prevention, appraisal, and failure are always the same.

QUALITY COST COLLECTION Now that the specific costs to be collected have been decided on, a method to collect them must be developed. Collection of quality costs should be the responsibility of the controller. The finance and accounting department is the cost collection agency of the company, and what is being done here is collecting costs. Besides, having the controller collect the costs adds credibility to the data. If top management is properly sold on the program, the controller will be charged with the task of heading this effort. With the help of the quality manager, the controller should review the list of costs to be collected, determine which of these are already available under the existing accounting system, and decide where additions to the existing system are needed. Sometimes, the simple addition of new cost element codes to the present charging system is sufficient. However, if necessary, the present system could be supplemented by separate inputs designed specially for this purpose.



Part II.E


Ideally, a complete system of cost element codes could be generated. They could be coded in such a way that the costs of prevention, appraisal, and internal and external failures could be easily distinguished and sorted (see Table 14.1). Then these codes could be entered into the labor cost collection system, together with the hours expended against the cost element or task represented by the code. The labor hours could later be easily converted to dollars. Scrap is an exception to this system of collecting quality costs as they are incurred. All work needs to be inspected, rejected, and dispositioned first. In many companies, the existing scrap reporting documents are forwarded to estimating, where the costs of expended labor and material are estimated to the stage of completion of the scrapped items. The accounting department should provide all collected quality costs to the quality function in a format suitable for analysis and reporting. Of course, training programs will be necessary to assure that all personnel are informed as to how to report their quality cost expenditures. The training should be repeated periodically and the collection system should be audited on a regular basis.

QUALITY COST SUMMARY AND ANALYSIS Quality costs can be summarized in many ways, such as by company, division, facility, department, or shop. They may be summarized by program, type of program, or all programs combined. What is the best way? The decision must be based on the specific needs of the organization. Analysis can include comparison of the total quality cost to an appropriate measurement base. Some commonly used bases are sales, cost input, and direct labor. Again, the base selected will depend on what is appropriate for the needs of the organization. Comparing quality costs to a measurement base will relate the cost of quality to the amount of work performed. An increase in quality costs with a proportionate increase in the base is normal. It is the nonproportionate change that should be of interest. The index “total quality cost over the measurement base” is the factor to be analyzed. The goal is to bring this index to a minimum through quality improvement. The index may be plotted so that trends representing present status in relation to past performance and future goals may be analyzed. Other methods of analysis include study of the effect that changes in one category have on the other categories and on the total quality cost. For example, was the increase in prevention costs effective in reducing failure costs? And was this reduction in failure costs sufficient to cause a reduction in total quality costs? This technique can provide insight into where the quality dollar can most wisely be spent. Increases in failure costs must be investigated to determine where costs must be expended to reverse a trend and reduce the total quality cost. Losses must be defined, their causes identified, and corrective action taken to preclude recurrence. Other existing quality systems, such as a defect reporting system, can be used in conjunction with the quality cost program to identify significant problems. The defect reporting system can help define the causes of scrap, rework, and other failure costs. While the losses are distributed among many causes, they are not uniformly distributed. A small percentage of the causes will account for a high percentage of the losses. This is the Pareto principle, where these causes are the



vital few as opposed to the trivial many. Concentration on prevention of the vital few causes will achieve maximum improvement at a minimum of cost. This quality improvement tool will have the effect of improving quality while reducing costs.

QUALITY COST REPORTING

Part II.E

There are almost as many ways to report quality costs as there are companies reporting them because how they are reported depends on who they are reported to and what the report is trying to say. The amount of detail included in the quality cost report generally depends on the level of management the report is geared to. To top management, the report might be a scorecard, depicting the status of the quality program through a few carefully selected trend charts—where it has been and the direction it is heading. Savings over the report period and opportunities for future savings might be identified. To middle management, the report might provide quality cost trends by department or shop to enable identification of areas in need of improvement. Reports to line management might provide detailed cost information, perhaps the results of a Pareto analysis identifying those specific areas where corrective action would afford the greatest improvement. Scrap and rework costs by shop also provide valuable information when included in reports to line management. Again, how quality costs are reported depends to a large extent on who they are reported to and what the report is trying to say.

USING QUALITY COSTS Once the quality cost program is implemented, it should be used by management to justify and support improvement in each major area of product or service activity. Quality costs should be reviewed for each major product line, manufacturing area, service area, or cost center. The improvement potential that exists in each individual area can then be looked at and meaningful goals can be established. The quality cost system then becomes an integral part of quality measurement. The proper balance is to establish improvement efforts at the level necessary to effectively reduce the total cost of quality, and then as progress is achieved, adjust it to where total quality costs are at the lowest attainable level. This prevents unheeded growth in quality costs and creates improved overall quality performance, reputation, and profits. Still another benefit to be gained from a quality cost program is its ability to be used as a budgeting tool. As costs are collected against quality cost elements, a history is generated. This history can then be used to determine the average cost per element. In other words, depending on how detailed the elements are that have been established, what the organization has been spending for various functions or tasks will be identified. This information can be used as the basis for future quotes and estimates. Budgets can be established for each element. Then, going full circle, the actuals collected against these elements can be bounced against the budget amounts to determine budget variances. Action can then be initiated to bring over- or under-running elements into line.



Part II.E

QUALITY IMPROVEMENT AND REDUCING QUALITY COSTS The key factor in the reduction of quality costs is quality improvement, and a key factor in quality improvement is corrective action. Quality costs do not reduce themselves. They are merely the scorecard. They can tell you where you are and where your corrective action dollar will afford the greatest return. Quality costs do identify targets for corrective action. Once a target for corrective action is identified, through Pareto or other methods of quality cost analysis, the action necessary must be carefully determined. It must be individually justified on the basis of an equitable cost tradeoff. You do not want to resolve a $500 problem with a $5000 solution. At this point, experience in measuring quality costs will be invaluable for estimating the payback on individual corrective action investments or quality improvement projects. Cost–benefit justification of corrective action and quality improvement projects should be a continuing part of the quality management program. Some problems have fairly obvious solutions, such as the replacement of a worn bearing or a worn tool, that can be fixed immediately. Others are not so obvious, such as a marginal condition in design or processing, and are almost never discovered and corrected without the benefit of a well-organized and formal approach. Marginal conditions usually result in problems that can easily become lost in the accepted cost of doing business. Having an organized quality improvement program and corrective action system, justified by quality costs, will reveal such problems for management’s visibility and action. The true value of corrective action is that you only have to pay for it once, whereas failure to take corrective action may be paid for over and over again.

QUALITY COST PRINCIPLES AND LESSONS Traditional quality cost methods have been around a long time—about half a century. These principles still apply today and will for the foreseeable future. However, through our experiences with quality costs over that time, some useful lessons learned can be identified that can be applied in the future. The first lesson is that speaking the language of money is essential. For a successful quality effort, the single most important element is leadership by upper management. To gain that leadership, some concepts or tools could be proposed, but that is the wrong approach. Instead, management should first be convinced that a problem exists that requires their attention and action, such as excessive costs due to poor quality. A quality cost study, particularly when coupled with a successful pilot quality improvement project, is a solid way to gain management support for a broad quality improvement effort. Excessive cost is a quality-related hot button for management. Loss of sales revenue is another. The second lesson learned is that quality cost measurement and publication do not solve quality problems. They must be used. Improvement projects must be identified, clear responsibilities established, and resources provided to diagnose and remove the cause of problems, as well as other essential steps. New organizational machinery is needed to attack and reduce the high costs of poor quality.



Part II.E

The third lesson is that the scope of traditional quality costs should be expanded. Traditionally, quality costs have emphasized the cost of nonconformities. Important as this cost is, we also need to estimate the cost of inefficient processes. This includes variation of product characteristics (even on conforming products), redundant operations, sorting inspections, and other forms of non-value-added activities. Another area to be considered is the cost of lost opportunities for sales revenue. The fourth lesson is that the traditional categories of quality costs have had a remarkable longevity. About 50 years ago, some pioneers proposed that quality costs be assigned the categories of prevention, appraisal, and failure. Many practitioners found the categories useful and even devised ingenious ways to adapt the categories beyond manufacturing, as in engineering design, and also to the service sector, as in financial services and healthcare. The principles still work today. The difference is in their additional applications. Quality costs have expanded to become a principal management and quality improvement tool. Definitions and standards have been developed and refined along with techniques and methods for implementation. Quality cost principles and concepts have been expanded to include lessons learned over the past half century, with applications now including the software and service sectors. The quality cost program is the bridge between line and executive management. It provides a common language and a measurement and evaluation system that shows how quality pays in increased profits, productivity, and customer acceptance.



Chapter 15

Part II.F

F. Quality Training

Quality Training. Identify and define key elements of a training program, including conducting a needs analysis, developing curricula and materials, and determining the program’s effectiveness. Body of Knowledge II.F

INTRODUCTION To keep an organization healthy, its people must be continually trained in new concepts and techniques. The late Frank Gryna epitomized the role of quality engineer as leader and trainer. In addition to the college students he helped educate and the adult workers he trained, he inspired hundreds of professionals to become passionate, skillful carriers of the quality torch as trainers. Gryna (1988) stated “the need is to extend training in quality-related matters to personnel in all functions.” He stated that while U.S. companies [formerly] trained only quality-related specialists in quality, the Japanese, from the very beginning of their industrial renaissance in 1946, targeted all departments, and that “this difference in training contributed to a quality crisis” for the United States. Training may be formal or informal, large scale or small. Although it is usually delivered by instructors in classrooms, training can be done through directed selfstudy with workbooks or computer guidance. A powerful training method in the areas of intellectual skills and human relations is mentoring. The mentor provides guidance, inspiration, and motivation. Likewise, well-structured improvement projects can include a training component. Project leaders are given special instructions to ensure that team members learn various skills as they complete the project. For details see the section on facilitation in Chapter 5. If the training task is small, it may involve only two people: an apprentice working alongside a master. At the other extreme it may require a fully staffed training and development department with a budget in the millions. In some companies, such as McDonalds and Motorola, this department is called a corporate university, and manages dozens of instructors delivering courses, seminars, exercises, and workshops year round. The middle ground would be you, a quality 116


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engineer who must organize a small training program yourself, or help a task team build a larger one.

DEVELOPING A TRAINING PROGRAM Essentially the same development process must be used, regardless of size. Many training programs are organized in five phases, as follows: 1. Assess the need for training 2. Design a curriculum, or training plan

4. Implement the plan, that is, deliver the instruction 5. Evaluate effectiveness of the training

Part II.F

3. Develop the lesson plans and training materials

This is called the ADDIE model, using the first letter of the first word of each phase. Some have proposed surrounding these five phases with a beginning called “customer identification” and an ending called “maintain beneficial outcomes,” resulting in the CADDIEM model. Many other variations are used, as illustrated by the following success story.

Training Case Study Number One At Tennessee Eastman Company in 1980, a companywide training program evolved from concern about poor quality attitudes. The project was reported by Hill and McClaskey (1980) (available at www.asq.org/qic). 1. Determining the purpose. This phase involved upper and middle management, numerous operational units, and the training department. The stated purpose was not to create a training program, but to improve overall quality performance. Only after several weeks of discussions and surveys was a training program decided on. 2. Developing alternatives that will achieve the purpose. Through the use of focus groups, brainstorming, and exchange of memos, several dozen alternatives were proposed. Seven were selected for further study. Two of the seven were: • Require position guides that include quality responsibilities • Create a central quality organization to coordinate development and dissemination of information and requirements. 3. Analyzing the alternatives. A set of five weighted criteria was developed, including such items as “chance of completion,” and “flexibility of format.” The alternative with the highest score was “teach about quality responsibilities.” 4. Designing the selected program. Many elements were specified. Some typical design parameters were:



• Create awareness of quality responsibilities • Cover total learning needs in quality awareness • Be adaptable to specific needs • Be used only by people requiring the knowledge • Be portable and usable near work area

Part II.F

• Have a maximum length of two hours After the design criteria were set, a survey was taken of all employees, with 40 potential courses listed. Using the survey, the task force proceeded to develop 16 new courses. 5. Implementing the solution. The team proceeded to write outlines and scripts, identify needed materials, and publish results, calling on others for help as needed. Teachers were selected and trained, a budget was set up, a cost tracking system was created, and the courses were put in place for delivery. The courses ran for several years with periodic updates. 6. Evaluating the results. Pre- and post-tests were regularly used to determine how much was learned.

NEEDS ANALYSIS If large-scale training is contemplated, spend more time than you think necessary in assessing the needs. Stay open-minded as to possibilities. A thorough needs analysis can prevent wasting of large amounts of time and money on ineffective or inadequate training. For example, you might start out thinking a training program is needed, only to discover through needs assessment that your problem is solved by organizational realignment. The methods of quality function deployment (QFD) are very suitable to needs assessment. One key QFD tool is the matrix chart, which can be used to organize the results of surveys and focus groups. See Chapter 7 for more details about QFD.

DEVELOPING CURRICULA AND MATERIALS The curriculum flows naturally from the needs analysis. It states learning objectives and how they will be achieved in each training event, whether by lecturer, demonstration, role play, or other method. Suppliers, customers, or even competitors may share their curriculum ideas. Subject matter experts and training professionals need to partner in this phase because of its highly technical and subject-specific nature.

Write Lesson Plans Once the curriculum is set, the lesson plans flow rather naturally (it helps to have experience or a good mentor). There are numerous issues to resolve, such



as detailed content, sequencing, depth, breadth, review points, quizzes, and demonstrations.

Choose Training Materials

Part II.F

Finding suitable materials is not a problem. However, selecting from the plethora of artifacts, paper and electronic media, simulations, workbooks, role plays, and so on, might be a problem. Remember that training can be structured in many ways. Formal classrooms may be appropriate but often the best training area is actually the workplace, with carefully designed instructional aids so that training occurs as the work is done. Creativity and imagination are definitely in order. For developing intellectual skills such as facilitation and conflict resolution, role playing is highly recommended. It is a good way to increase the learners’ involvement in the process. Designing good role plays can be time-consuming. But Stolovitch (1992) explained how role plays and simulations can be developed during the learning process in certain situations. Book discussion groups can focus management attention on timely issues with little or no development cost. A rapidly growing body of training material is now available electronically, through both video and computer. In fact these two technologies are gradually converging. A number of different organizations offer products and assistance in this exciting new area of training materials. Web search engines such as Google and Yahoo will find many entries dealing with this rapidly changing area.

Select and Train the Trainer Both technical competence and teaching skills are mandatory! An experienced quality engineer might need special “how to teach” training, since technical competence does not ensure teaching skill. “Train the trainer” programs can be anything from a colleague giving informal mentoring to an eight-week off-site course. Remember, students must respect both teaching ability and competence for much learning to take place.

DETERMINE EFFECTIVENESS Referring to the ADDIE model, evaluation (step 5) and needs analysis (step 1) are closely related. Both are learning experiences for the training team. It is very important for credibility to demonstrate that something useful has been accomplished. And if the outcome was poor you need to know quickly to take immediate corrective action. Some informal evaluation can occur while the course is under way. Especially if the instructor is new, a trusted colleague or mentor can help with discussions and role play exercises, and can visit with attendees during breaks. The single most prevalent (and probably most cost-effective) form of evaluation is the preand post-test of knowledge. Attendee evaluation forms (rate the instructor, rate the course) have been widely used in the past but this technique is not recommended as there is too much



Part II.F

Table 15.1

Five different levels of evaluation.

Level Name

Question

Techniques

1

Reaction

How did learners feel?

Post-instruction questionnaires or interviews. Learners report impressions of instructor, curriculum, facilities, and content.

2

Learning

What did learners retain?

Pre-test and post-test, checking for gains in either knowledge or performance.

3

Behavior

Did learners change?

Assessors must collect data at the workplace to evaluate changes in skill and performance.

4

Organizational

Is the impact beyond learner?

Large-scale surveys of morale, product quality, turnover, and so on, followed by executive conferences.

5

Return on investment

Is there an effect on the bottom line?

Analyze financial data carefully constructed by professionals. Six Sigma includes this element in program evaluation.

room for subjectivity. This is known as level 1 evaluation in Donald Kirkpatrick’s hierarchy (Kirkpatrick 2006), shown with one additional level in Table 15.1. The fifth level is most informative but also quite difficult and expensive to achieve.

Why Training Programs Fail According to Gryna (1988), 10 reasons why training programs fail are: 1. Cultural resistance by line managers 2. Doubt as to the usefulness of the training 3. Lack of participation by line managers 4. Technique rather than problem orientation 5. Inadequacy of leader/instructor 6. Mixing of participant levels 7. Lack of application during the course 8. Overly complex language 9. Lack of participation by the training function 10. Operational and logistical deficiencies Consider reason #7 in more detail. To quote Gryna, “The ideal approach is to design the course so that the participants must apply the training during the course. One of the best learning experiences is the application of the material being taught.



This was successfully done during World War II in the area of work-simplification programs. More recently, value engineering seminars often have a project included as part of the seminar. Quality circles also use the concept.” In the years since Gryna published those words, the scope and effectiveness of training for quality in the United States has greatly increased.

What We Know About Adult Learners Most training programs are directed toward adults. Knowles (1996) pointed out significant facts about how adults learn. Three of his conclusions are:

2. Adults buy in to training when it is supported on the job by supervisors and management.

Part II.F

1. Adults decide for themselves what is and isn’t important to learn. In order to be sure learning takes place, the trainer should state specifically what should be learned and how it relates to on-the-job performance.

3. Adults who are happy in their jobs are more receptive to training; and adults who are well-trained for their jobs are happier employees.

Case Study Number Two This case illustrates pitfalls # 4 (technique orientation), #5 (inadequacies of leader/ instructor), #7 (no immediate application), and #8 (language too complex). It also demonstrates the importance of Knowles’s conclusion #1 above. The manager of an engineering design unit decided that his engineers should learn how to use statistics in their daily work. A recent PhD in statistics was recruited from a nearby university. He loved the beauty of statistics and mathematics. Without really thinking it through, he assumed that the attendees shared his own values. Since they all had engineering degrees, he knew they possessed the needed mathematical background, and he unconsciously assumed they could pick up the jargon. Neither the instructor nor his sponsor checked to see what the attendees wanted, and so he started teaching much the same techniques and theories, using the same language, as with his undergraduate students. After two days, the grumbling became so loud that the instructor was directed to meet with lead engineers to find out what was going wrong. A complete redirection of the course was made, but half the course was over before it could be implemented, and it was impossible by then to build any rapport between instructor and class. Ultimately the effort was judged a complete failure, and the practicing engineers’ prior prejudices against “statistics and statisticians” were reinforced. It was a negative experience all round and could have been prevented by more sensitive planning and understanding before the course began. Another probable reason for the failure was a lack of awareness on the part of both instructor and sponsor of the difference between training and education, which is highlighted in Figure 15.1. The instructor was used to working in the education environment, but the sponsor was expecting a training program.



Training Providing skills and techniques for immediate application in a job or related (for example, lifesaving, pottery-making) situation. Generally sponsored by employers for immediate payoff.

Figure 15.1

Education Developing one’s knowledge and understanding, with a goal of enriching one’s life in an unpredictable future. Generally paid for by taxpayers, foundations, parents, and long-term student loans.

Training versus education.

Part II.F

A FINAL THOUGHT . . . We leave you with this thought: in a sense, life is a process of continually alternating between two kinds of events—learning, and doing what you have learned. When you do a training event, you are also helping someone else to learn. Do it well and you enrich the learner’s life as well as your own.

SUMMARY OF PART II Part II is all about the quality system. The first three chapters of Part II address system elements, documentation, and standards/guidelines. The last three chapters address auditing, training, and cost of quality. The quality system is an enabling mechanism that provides the framework for achieving quality. It must be formal, it must be detailed, it must be explicitly documented, and it must be understood by the people who use it. There is more than one way to approach the design of a quality system. Two quite different approaches are the “requirements” approach of the ISO 9001 standard and the “guidelines” approach of the Baldrige Award. ISO 9001 is a worldwide standard of requirements. It uses a controlled vocabulary spelled out in ISO 9000. It is complemented by guidelines in ISO 9004, which allows for nonrequired improvement activities. Those who wish to be registered must prepare all their materials and then submit to a formal audit verifying that they are doing what they say they are doing. The Baldrige Award program is completely voluntary and utilizes guidelines and examiners to choose national winners in various categories each year. Quality auditing is a cousin of quality engineering. While there is overlap between the two specialties, each has its own unique role. Auditing is a critical part of maintaining a quality system. Cost of quality concepts and tools help quality professionals justify our role in the language of top management: money. The basic concept is to track costs throughout the entire organization and then show how spending money on quality activities saves much more money in reduced failure and waste. Many tools are provided to make this quality system an effective one. Training is essential to maintaining organizational excellence, and quality engineers may be asked to put on their training hats from time to time. Needs must be assessed and the training curriculum must then be designed. After assembling all the necessary materials and conducting the training, its effectiveness is assessed.



Part III Product and Process Design Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20

A. Classification of Quality Characteristics B. Design Inputs and Review C. Technical Drawings and Specifications D. Design Verification E. Reliability and Maintainability

Part III

The five chapters in Part III cover the different elements that quality engineers use in quality initiatives involving products and processes. The areas covered in this section include classification of quality characteristics (as opposed to product defects, which are discussed in Part IV), design inputs and review elements, elements of technical drawings and specifications, design verification to ensure fitness for use, and reliability and maintainability.

123



Chapter 16 A. Classification of Quality Characteristics

Define, interpret, and classify quality characteristics for new products and processes. (Evaluate).

Part III.A

Body of Knowledge III.A

Quality characteristics are features that describe the fit and function of a product or process and aid in differentiating between items of a given sample or population. To differentiate items from each other, and/or to compare items to a standard, measurements and/or comparisons are used. Variables data are represented by direct measurement on a continuous scale. Attributes data are most often discrete data usually reported in the form of counts. The counts are classified by category with the most common categories being pass/fail, go/no-go, and accept/ reject. (For more details on variables, attributes, and continuous and discrete data see Chapters 27, 32, and 34).

MEASUREMENT OF VARIABLES DATA Measurement is the process of evaluating a property or characteristic of an object and describing it with a numerical or nominal value. A quality characteristic is referred to as a variable if it is measurable over a continuous scale. For example, in healthcare a patient’s temperature can be measured with an electronic thermometer. Other examples could include measurements related to weight, length, diameter, or cost.

MEASUREMENT OF ATTRIBUTES DATA If the quality characteristic of interest can’t be directly measured, then each item under inspection is often classified into one of two or more categories. For example, items inspected may be classified as conforming or nonconforming. Each product unit is assigned one of these two labels according to inspection operation results. It is then possible to derive a numerical measure of process quality using a quantitative scale. The numerical measure is achieved by calculating the fraction nonconforming as the ratio between the number of units labeled as nonconforming and the total number of units inspected. When the item inspected can be classified into 124


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one of exactly two possible categories, the binomial distribution is often appropriate to model this situation. (See Chapters 27 and 32 for more details on attributes data and the binomial distribution.) Another commonly used attribute quality characteristic is the number of nonconformities (or number of defects) observed on an item inspected. In this situation, a single item inspected may have more than one nonconformity or defect. For example, a car door panel may have more than one scratch, dent, or discoloration. These would be considered nonconformities on the single item inspected, in this case the car door panel. The number of nonconformities is often well modeled by the Poisson distribution. (See Chapters 27 and 32 for more details on number of nonconformities and the Poisson distribution.)

Limit Gages

Go

Figure 16.1

Part III.A

A common method of inspection by attributes involves the use of limit gages, also known as fixed limit gages or go/no-go gages. Limit gages are made to sizes essentially identical with the design specification limits of the dimension to be inspected. If a specific gage can properly mate with a part, then the part can be assembled with another part whose physical boundaries do not exceed those of the gage. Consequently, the part is acceptable for assembly. Limit gages designed to identify this condition are called go gages. (See Figure 16.1.) The go end of a go/no-go gage is designed to check the characteristic at the maximum material condition (minimum size for interior features, maximum size for exterior features). The maximum material condition produces the minimum clearance required for assembly. The no-go end is designed to detect conditions of excessive clearance. It checks the characteristic at its minimum material condition. A part will not mate with a no-go gage unless the actual condition of the part feature is below the specified minimum. Thus, if the no-go gage mates with the part, then the part dimension is out of specification and the part should be rejected. In practice, go/no-go gages are used together and often appear at opposite ends of an inspection instrument. An acceptable part should mate with the go end but should not mate with the no-go end. Parts that mate with neither or both ends do not meet design specifications and should be rejected. Other than gauging, most methods of inspection by attributes are largely subjective and depend on the ability of human inspectors to make the right decisions. In many cases inspection by attributes involves visual characteristics, such as color, shape, or smoothness, and other visual defects.

No-go

Go/no-go gage to check the diameter of a shaft.



Chapter 17

Part III.B

B. Design Inputs and Review

Identify sources of design inputs such as customer needs, regulatory requirements, etc. and how they translate into design concepts such as robust design, QFD, and Design for X (DFX, where X can mean six sigma (DFSS), manufacturability (DFM), cost (DFC), etc.). Identify and apply common elements of the design review process, including roles and responsibilities of participants. (Analyze) Body of Knowledge III.B

DESIGN BACKGROUND AND DEFINITION Design is a term that describes the thought processes, procedures, tools, documentation, and specifications associated with products and processes. Designs are developed to document and ensure compliance with customer expectations as they relate to operational capabilities and characteristics of products and processes.

THE PRODUCT/PROCESS DEVELOPMENT LIFECYCLE Whether for products or processes, designs progress through phases. Design phases are linked to phases within the product/process development lifecycle, wherein the product/process development lifecycle typically includes the following: • Definition phase. In this phase, a problem or opportunity is clearly defined, documented, refined, and consensus on the definition is reached among stakeholders.

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• Specification phase. In this phase, specifications are set that lead to, or provide, a product or process desired by the customer. Specifications are commonly applied to characteristics that affect the form, fit, or function of a product or process. Specifications also are commonly applied to characteristics that affect the reliability of a product or process to function in a given environment, within a given range of temperature, or for a given period of time or cycle of operation. • Concept phase. In this phase, all possible solutions to solve the problem or exploit the opportunity are explored. During the concept phase, feasible solutions are identified and nonfeasible solutions are dismissed. • Detailed design phase. In this phase, specifications and the most feasible concept are used as the basis for development of detailed plans for a product or process design. • Prototype phase. In this phase, a working model of the detailed design is fabricated and tested in a laboratory or development environment for its ability to perform or operate as intended.

Part III.B

• Production phase. In this phase, following successful development, testing, and refinement of a prototype, production units of the design are produced in sufficient volume to satisfy customer demand. Production phase units are produced not in a laboratory or development environment as were prototype units, but are produced with tools, equipment, methods, and procedures used on the shop floor or service delivery area, and these production units are produced by regular production or service delivery personnel. • Distribution phase. In this phase, the product or process enters the supply chain for sale and distribution. • Normal use phase. In this phase, the product or process is released to the customer for use in its intended role or function. During normal use, products and processes require normal maintenance, repair (warranty and non-warranty), and customers frequently require technical assistance and support. • Obsolescence and disposal phase. In this phase, products and processes lose their usefulness due to normal wear, catastrophic failure (planned or unplanned), introduction of enhancements to an existing design, or changes in technology. As products and processes become obsolete, the original designer must consider whether or how to provide enhancements enabling extended life and/or the designer must consider how to safely and ethically address disposal. How a design links with the product/process development lifecycle (that is, which specific design phases are used, and how the work of various design phases are related to the product/process development lifecycle) depends completely on the model used as a basis for a design. It should be noted that there are numerous models or approaches for design, far too many models to provide an exhaustive list in this book. One model for review of designs, however, is well developed, sufficiently universal, applies to products or processes, and is known as the Systems


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Engineering Technical Review Process as described in the United States Navy, Naval Air Systems Command (NAVAIR) Instruction 4355.19C (April, 2006). Available at http://www.navair.navy.mil/kms/41g.

DESIGN REVIEW In accordance with NAVAIR Instruction 4355.19C, there are 12 phases of design review as follows: • Initial Technical Review (ITR) • Alternative Systems Review (ASR) • Systems Requirements Review (SRR) • Technology Readiness Assessment (TRA) • System Functional Review (SFR) • Preliminary Design Review (PDR)

Part III.B

• Critical Design Review (CDR) • Test Readiness Review (TRR) • Flight Readiness Review (FRR) (for airborne systems) • System Verification Review/Production Readiness Review (SVR/PRR) • Physical Configuration Audit (PCA) • In-Service Review (ISR) Depending on the type and complexity of the design, any or all of the design reviews may be completed. The depth of detail, analysis, and documentation should increase as the design reviews progress toward completion. Design reviews are not intended for solving design-related problems but rather to verify completion of problem-solving activities by cross-functional teams. Discovery of too many design-related problems during a design review may indicate that a design review is being conducted prematurely. Although the product development group is responsible for creating a design, no one group can provide all the necessary assurance that the design is adequate. Therefore, design reviews should be conducted periodically by a cross-functional team until the design and process are finalized. Quality and manufacturing should be active participants in the review process. Suppliers also should participate if possible. Early sourcing commitments enable suppliers to attend the design reviews and contribute their expertise prior to investing in expensive tooling. Drawings should comply with applicable standards for drafting, dimensioning, and tolerances. At each review, the design must be considered from several different viewpoints: • Reliability. Will the failure rate be sufficiently low? • Quality engineering. Can the design be adequately inspected and tested?


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• Field engineering. Are proper installation, maintenance, and user-handling features included in the design? • Procurement. Can the necessary parts be acquired at acceptable costs, delivery schedules, and quality levels? • Materials engineering. Will the selected materials perform as expected? • Tooling engineering. Is the equipment capable of meeting the specified tolerances on a consistent basis? • Packaging engineering. Can the product be shipped without damage? • Outside consultants. Have appropriate outside consultants been called for when necessary? • Customer. Should a customer representative participate in the design reviews for military applications and original equipment manufacturers?

DESIGN INPUTS Designs for products and processes are influenced by factors known as inputs. Inputs are simply requirements placed on products and processes that relate to:

Part III.B

• Other design engineers. Are other design engineers needed when there are tight tolerances to mating components or critical system interfaces?

• Customer needs and expectations (for example, time of delivery, cost, performance characteristics) • Regulatory requirements (for example, safety of end users, safety of production/service delivery personnel, use of hazardous chemicals, distribution to unauthorized personnel/vendors, control of sensitive technology) • Patents and technology licensing (for example, protections for existing designs owned by competitors) • Product/process capabilities • Product/process reliability

Translating Design Inputs into Basic Design Concepts The design inputs identified above translate into basic design concepts relevant to quality engineering, particularly when the nature of a design emphasizes a specific characteristic. When a specific characteristic is emphasized, the nature of the design may be considered “constrained” wherein the design input must either guide design efforts (such as the case with design for Six Sigma), or the design input must constrain the product or process resulting from the design (such as the



case with design for cost). When a design does emphasize a specific characteristic, the design is referred to as “design for X,” where X may be Six Sigma, cost, manufacturability, or reliability. Awareness of the basic design concepts and how they relate to the design process are included within the Certified Quality Engineer Body of Knowledge and are addressed as follows: Design for Six Sigma. Design for Six Sigma (DFSS) is not to be confused with the well-known DMAIC approach to Six Sigma. While DMAIC Six Sigma focuses on solving problems in existing products or processes, DFSS focuses on eliminating problems before they occur. Design for Cost. Design for Cost (DFC) is a design constraint that begins with cost targets as drivers of the design process. DFC differs from most commonly used traditional design processes, which typically tally development costs at the end of the design process with little regard for cost targets.

Part III.B

Design for Manufacturability. Design for Manufacturability (DFM) is an approach to the design of products and processes that focuses on cost-effectiveness, simplification, optimization, and mistake-proofing to enhance manufacturability. DFM applies both to the product/process design as well as the use/selection of manufacturing technology. Design for Reliability. Design for Reliability (DFR) is an approach to design that focuses on development of products and processes able to perform under specified conditions, in a specified environment, and for specified periods of time.

Related Design Inputs and Basic Design Concepts Quality Function Deployment. Quality Function Deployment (QFD) provides a framework for the design process that captures the “voice of the customer” (VOC) via a series of matrices. Once captured, the VOC is used to guide design efforts to ensure that customer expectations are met. See page 65 for more information on QFD. Concurrent Engineering. Concurrent engineering is practice of the design function and associated activities by a team of engineers, technicians, management, and administrative personnel such that all aspects of the design phases are considered simultaneously.



Chapter 18 C. Technical Drawings and Specifications

Interpret technical drawings including characteristics such as views, title blocks, dimensioning, tolerancing, GD&T symbols, and so on. Interpret specification requirements in relation to product and process characteristics. (Evaluate)

Part III.C

Body of Knowledge III.C

DIMENSIONING AND TOLERANCING It is expected that drawings have dimensions that provide detailed information about sizes, shapes, and the location of different components and parts. It is also expected that part and component dimensions show acceptable variation. To produce any part or component to an exact dimension is nearly impossible, except by remote chance. Variations in materials, machines, manufacturing parameters, and humans make it necessary that dimensions have acceptable variations. Such variation is referred to as tolerance. Higher quality requires tighter tolerances that, in turn, require more expensive and strict production and inspection procedures to obtain. There are two types of tolerances: unilateral tolerance and bilateral tolerance. Unilateral tolerance specifies allowable variation in a dimension from a basic or nominal size in one direction in relation to that basic size. For example: 2.000+0.000/-0.005 inches describes an allowable variation only in the lower limit: unilateral tolerance. Specifications on a part with this tolerance will be 2.000 inches and 1.995 inches as desired upper and lower limits, respectively. On the other hand, 2.000+0.005/-0.005 inches describes a bilateral tolerance. It does specify a dimension with allowable variations in both directions of the basic size. Specifications on a part with such bilateral tolerance will be 2.005 inches and 1.995 inches as desired upper and lower limits, respectively.

131



Geometric Dimensioning and Tolerancing (GD&T) Geometric tolerancing defines tolerances for geometric features or characteristics on a part. Figure 18.1 shows some of the geometric dimensioning symbols as defined in ANSI Y14.5M. Figure 18.2 illustrates the interpretation of a geometric tolerance on a drawing. The limit dimensions of the simple cylindrical piece at the top of Figure 18.3 define the maximum and minimum limits of a profile for the work. The form or shape of the part may vary as long as no portions of the part exceed the maximum profile limit or are inside the minimum profile limit. If a part measures its maximum material limit of size everywhere, it should be of perfect form. This is referred to as the maximum material condition (MMC) and is at the low limit for a hole or slot but at the high limit for parts such as shafts, bolts, or pins. If it is desired to provide greater control on the form than is imposed by the limit dimensions, then certain tolerances of form must be applied. In most cases, these tolerances appear in the form of notations on the drawing as is illustrated at the bottom of Figure 18.3.

Part III.C

Geometric Symbols Straightness

Concentricity

Flatness

Profile of a line

Parallelism

Profile of a surface

Perpendicularity

True position

Angularity

Runout

Roundness

Total runout

Cylindericity Other Symbols Maximum material condition (MMC)

M

Least material condition (LMC)

L

Diameter Datum is A Figure 18.1

–A–

Some geometric tolerancing symbols.


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 18: C. Technical Drawings and Specifications 133

M

0.005

A

B

C

Tertiary datum Secondary datum Primary datum Geometric tolerance Modifier Shape of tolerance zone Geometric feature/characteristic Figure 18.2 Interpretation of a geometric tolerance on a drawing. Part Y

50.15 49.85

2.005 1.995

Flat within 0.05 mm

This face parallel to A within 0.05 mm FIM*

1.000 .990

25.00 24.90

A

50.15 49.85 Straight within 0.05 mm

Part III.C

25.00 24.90

(in.) 1.000 .990

(mm)

Part X

2.005 This face parallel 1.995 to A within .002 in. Straight within FIM* .002 in.

A

Flat within .002 in.

*FIM = Full indicator movement

Figure 18.3

Part drawing with and without tolerances of form.

Reprinted with permission of the Society of Manufacturing Engineers, Manufacturing Processes and Materials, 4th Edition, Copyright 2000.

Positional Tolerances Positional tolerancing is a system of specifying the true position, size, or form of a part feature and the amount it may vary from the ideal. The advantage of the system is that it allows the one responsible for making the part to divide tolerances between position and size as he or she finds best. The principles are illustrated for two simple mating parts in Figure 18.4. The basic dimensions without



Part III.C

134 Part III: Product and Process Design

tolerances are shown at the bottom and right side of each part. Beneath the size dimension for holes or posts is a box with the notations for positional tolerancing. Actually, a number of specifications are possible but only one set is shown here as an example. The circle and cross in the first cell of the box is the convention that says the feature has a positional tolerance. Part I in Figure 18.4 introduces the idea of the MMC utilized in most positional tolerancing. This is designated by the letter M in a circle and means that the smallest hole (12.70 mm or .500 in.) determines the inner boundary for any hole. The “∅ 0.20 mm (.008 in.)” notation in the box specifies that the axis of any minimumsize hole must not be outside a theoretical cylinder of 0.20 mm (.008 in.) diameter around the true position. A 12.50 mm (.492 in.) diameter plug in true position will fit in any 12.70 mm (.500 in.) diameter hole with its axis on the 0.20 mm (.008 in.) diameter cylinder. Any hole that passes over such a plug is acceptable, provided that its diameter is within the high and low limits specified. The letter “A” in the specification box designates that the theoretical cylinder bounding the hole axes must be perpendicular to the datum surface carrying the “A” flag. Features usually are referred to with three coordinate datum surfaces, but for simplicity, in this case, the holes are related only to each other and surface “A” and not to the sides of the part. Part II of Figure 18.4 introduces the idea of zero maximum material condition specified by “∅ 0.000” before the MMC symbol. This means the axis of the largestdiameter post (12.50 mm [.492 in.]) must be exactly in the true position, but smaller sizes of posts may vary in position as long as they do not lie outside the boundary set by the largest. Thus, if the posts are held to a tolerance smaller than the 0.20 mm (.008 in.) specified, say to a tolerance of 0.05 mm (.002 in.), the difference (0.15 mm [.006 in.]) is then available for variations in post positions. The advantage of zero MMC is that only one limit of the feature, in this case the lower limit of the post diameter, needs to be checked along with position.

4×Ø

12.70 mm (.500 in.) 12.90 mm (.508 in.)

4×Ø 4 holes

12.50 mm (.492 in.) 12.30 mm (.484 in.)

A 63.50 mm (2.500 in.)

Figure 18.4

M

A Part II

38.10 mm (1.500 in.)

Ø 0.000

38.10 mm (1.500 in.)

Ø 0.20 mm (.008 in.) M A Part I

4 posts

A 63.50 mm (2.500 in.)

Two parts dimensioned with positional tolerances.

Reprinted with permission of the Society of Manufacturing Engineers, Manufacturing Processes and Materials, 4th Edition, Copyright 2000.



Chapter 19 D. Design Verification

Identify and apply various evaluations and tests to qualify and validate the design of new products and processes to ensure their fitness for use. (Evaluate) Body of Knowledge III.D

Part III.D

Design verification consists of a series of evaluations and tests to validate the design and ensure that it is fit for use in its intended environment. The test methods, sample sizes, and acceptance criteria should be clearly specified during conceptual planning of a product or process, and the design should be validated at each phase of the design review. When designing the tests, consider incorporating the following factors: • Dimensional wear, material fatigue, assembly process variation • Variation of critical characteristics throughout the range of the tolerances • Contamination • Environmental aging and extreme environmental conditions • Extreme customer usage, such as maximum loads or long duty cycles Test results should be analyzed using appropriate statistical methods, including reliability analysis. Reliability testing falls into four major categories, as delineated in MIL-STD-785B10: • Environmental stress screening to identify early failures due to weak parts or poor workmanship • Reliability growth tests, to be performed periodically between design conception and final production, to track the improvement of reliability and the resolution of reliability concerns • Reliability qualification tests to provide assurance that production units will meet requirements when they become available 135



• Production reliability acceptance tests to periodically verify that production units meet specified reliability requirements

Part III.D

A more detailed discussion of reliability and maintainability is provided in Chapter 20. Test failures should be carefully examined to determine the failure modes. Unanticipated failure modes must be added to the design failure mode and effects analysis (DFMEA). Of course, corrective actions and design improvements must be pursued if the test results do not meet the quality goals. Results of engineering evaluations, reliability tests, and other methods used to validate the design should be included in the design reviews. In addition, this information should be used to update the classification of quality characteristics. As is the case with many quality disciplines, the process of classifying characteristics should be iterative. Characteristics that are associated with unexpected failures may require reclassification as major or critical characteristics. Characteristics that perform as expected may be candidates for downgrading to minor characteristics. In all cases, involve the quality team in the discussions. There can be no substitute for the experience and process knowledge the team members bring to the design review process. More information on MIL-STD-785B10 can be found at http://assist.daps.dla.mil/quicksearch. Marsh (2004) provides a simple short discussion of canceled U.S. military specifications.



Chapter 20 E. Reliability and Maintainability

INTRODUCTION

Part III.E.1

This chapter focuses on estimating and predicting reliability and defines other reliability measures for repairable systems, such as maintainability and availability. Other chapters in this handbook focus on quality as a static characteristic of a product at the time it is released to the user. However, because reliability is a timedependent quality characteristic, traditional methods for quality control cannot be used to ensure product reliability and maintainability. Finally, this chapter discusses systems, techniques, and failure models for analyzing a system in order to determine its potential failures. Because reliability engineering is a broad field, it is impossible to cover the entire range of reliability topics in one chapter. This chapter focuses on reliability and maintainability definitions, analysis of failure data, design of systems for reliability, and maintainability and risk analysis using fault tree analysis (FTA), failure mode and effects analysis (FMEA), failure mode effects and criticality analysis (FMECA), and environmental stress screening (ESS). Reliability and maintainability as elements of product and process design will be viewed in four parts: predictive and preventive maintenance tools, reliability and maintainability indices, the bathtub curve, and reliability, safety, and hazard assessment tools.

1. PREDICTIVE AND PREVENTIVE MAINTENANCE TOOLS Describe and apply predictive and preventive maintenance tools and techniques to maintain and improve process and product reliability. (Analyze) Body of Knowledge III.E.1

Reliability is defined as the probability that a product or service will operate properly for a specified period of time (design life) under the design operating conditions. The main factors that lead to a system’s failure include the system’s design and configuration, the reliability of its components, the operating environment, 137



and the interactions among environmental factors, manufacturing defects, and preventive and scheduled maintenance. Further, reliability cannot be measured at the release time of the product but can only be predicted (Elsayed 2000). Therefore, it is extremely important to consider reliability during the design phase of a product or service because minor, major, and catastrophic failures result in economic consequences such as repairs or replacements, the loss of production or interruption of service, and potentially severe economic losses and the loss of life. Examples of major failures are: • Failure of a major link of a telecommunications network • Failure of a power generating unit • Failure of software for an air traffic control system The consequences of catastrophic failures are much more severe than minor or major failures, and may include the loss of human life and significant economic losses. Examples of catastrophic failures are:

Part III.E.1

• Explosions at the Chernobyl nuclear reactors site in the former USSR (Elsayed 1996) • Explosion of the space shuttle Challenger in 1986 • Failure of the space shuttle Columbia in 2003 Reliability also has a great effect on consumers’ perception of a manufacturer. For example, consumers’ experiences with automobile recalls, repairs, and warranties affect the manufacturer’s future sales. Another example shows the importance of reliability: 6.5 million tires were recalled after 46 deaths were attributed to the separation of the tread from the tire causing vehicles to skid or roll over.

Defining and Estimating Reliability Reliability usually is defined in terms of the probability that a product or service will perform properly under specified conditions for a specified period of time. Three important functions that are the result of traditional calculus derivations help quantify reliability: the reliability function, the failure time distribution function (sometimes referred to as the probability density function), and the hazard rate function (or instantaneous failure rate). Suppose N identical components are tested. During a specified time interval t, we observe x failures and (N – x) survivors. Because reliability is defined as the cumulative probability function of success, then at time t the reliability R(t) is: R (t ) =

( N − x) N

(20–1)

In other words, the reliability function (or survival function) at time t is the fraction of all components that have survived for a time greater than t. R(t) is also used as the estimate of the probability that a randomly selected component will survive


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 20: E. Reliability and Maintainability 139

for a time greater than t. In order to describe the distribution of failures, the cumulative distribution function (cdf) of failure F(t) can be defined as: F (t ) =

x N

(20–2)

(See Chapter 34 for more details on cumulative distribution functions.) The cdf given in (20–2) can be interpreted as: • The probability that a randomly selected unit drawn from a population fails by time t, or • The fraction of all units in the population that fail by time t. In addition, F(t) is the complement of R(t) and F ( t ) + R ( t ) = 1.

(20–3)

Equation (20–3) can be rewritten as: (20–4)

F (t ) = 1 − R (t )

(20–5)

or

Suppose N identical units are selected at random from a population described by F(t). Then NF(t) is the average (expected) number of failures through time t, and NR(t) represents the average (expected) number of survivors through time t. That is, we would expect NR(t) of the units to still be operational up to time t. A probability density function (pdf) that represents the distribution of failure time can be found by taking the derivative of equation (20–5) f (t ) =

dF ( t ) dt

=−

dR ( t ) dt

Part III.E.1

R (t ) = 1 − F (t )

(20–6)

The hazard rate function is defined as the limit of the failure rate as the time interval approaches zero. In other words, it provides an instantaneous rate of failure at some time t. The hazard rate function (also known as the instantaneous hazard rate or failure rate function) can be expressed as: h (t ) =

f (t ) Number of failures per unit time = Number of components tested per unit time R ( t )

(20–7)

Grouped Data In some situations, failure times are placed into time intervals and the individual failure times are no longer preserved. In this case, the failure times become



grouped data. The reliability, distribution function, failure density, and hazard rate are estimated from the grouped data. Suppose we wish to estimate the four quantities given in equations (20–1), (20–2), (20–6), and (20–7) in terms of a reliability test where N identical units are tested. First, record the number of failed units (xi) and the number of survivors (ni = N – xi) at time ti (where i = 1, 2, . . .). Next, R(t), F(t), f(t), and h(t) can be estimated as follows: n Reliability: Rˆ ( t ) = i N Cumulative distribution function: Fˆ ( t ) = 1 − Rˆ ( t ) Failure density: fˆ ( t ) =

Part III.E.1

Hazard rate: hˆ ( t ) =

ni − ni+1 , for ti < t < ti+1 (ti+1 − ti ) × N

fˆ ( t ) ni − ni+1 , for ti < t < ti+1 = (ti+1 − ti ) × ni Rˆ (t )

The caret (“hat”) on each term indicates an estimated quantity. There are several methods for estimating these functions if the data are ungrouped, censored, ungrouped and censored, or grouped and censored. The reader is referred to Ebeling (2005) for a complete discussion and derivations of these quantities and more.

EXAMPLE 20.1 Suppose that 300 light bulbs are subjected to a reliability test. The manufacturer would release the bulbs for distribution if the reliability of the bulb were 0.75 at 2000 hours of usage. The observed failures during 1000-hour intervals are shown in Table 20.1.

Solution: Using the previous equations we can determine the four functions: reliability function, distribution function, probability density function (failure density), and the hazard rate function. The results are shown in Table 20.2. To illustrate, consider the values for i = 2, where 1000 < t < 2000: n 286 Estimated reliability: Rˆ ( t ) = 2 = = 0 . 95333 N 300 Cumulative distribution function: Fˆ ( t ) = 1 − Rˆ ( t ) = 1 − 0 . 95333 = 0 . 04667 Failure density: fˆ ( t ) = Hazard rate: hˆ ( t ) =

n2 − n3

=

286 − 269

(t3 − t2 ) × N ( 2000 − 1000 ) × 300 n2 − n3

=

286 − 269

(t3 − t2 ) × n2 ( 2000 − 1000 ) × 2866

= 0 . 0000567

= 0 . 0000594

Continued



Continued

Table 20.1

Number of failures in the time intervals.

Upper bound (hours)

Number of failures, x

0

0

1000

14

2000

17

3000

21

4000

25

5000

31

6000

37

7000

40

8000

50

9000

65

Interval

Upper bound

Failures in the interval

Reliability

Cumulative distribution function

Survivors

Failure density

Hazard rate

i

ti

xi

ni

ˆ (t) R

Fˆ (t)

fˆ (t)

hˆ (t)

1

0

0

300

1.00000

0.00000

0.0000467

0.0000467

2

1000

14

286

0.95333

0.04667

0.0000567

0.0000594

3

2000

17

269

0.89667

0.10333

0.0000700

0.0000781

4

3000

21

248

0.82667

0.17333

0.0000833

0.0001008

5

4000

25

223

0.74333

0.25667

0.0001033

0.0001390

6

5000

31

192

0.64000

0.36000

0.0001233

0.0001927

7

6000

37

155

0.51667

0.48333

0.0001333

0.0002581

8

7000

40

115

0.38333

0.61667

0.0001667

0.0004348

9

8000

50

65

0.21667

0.78333

0.0002167

0.0010000

10

9000

65

0

0.00000

1.00000

Part III.E.1

Table 20.2 Reliability, cumulative distribution function, failure density, and hazard rate for the light bulb example.

These values can be easily calculated using a spreadsheet. The reliability function, cumulative distribution function, failure density function, and hazard rate function are displayed in Figures 20.1 through 20.4, respectively. The Continued



Continued

reliability function in Figure 20.1 indicates that the bulbs exceed the level of reliability set by the manufacturers. The distribution function in Figure 20.2 shows how unreliability grows with the passage of time. The failure density function is displayed in Figure 20.3.

1.0

0.8

R(t)

0.6

0.4

Part III.E.1

0.2

0.0 0

1000

2000

3000

4000

5000

6000

7000

8000

9000

6000

7000

8000

9000

Bound (hours)

Figure 20.1 Reliability function versus time.

1.0

0.8

F(t)

0.6

0.4

0.2

0.0 0

1000

2000

3000

4000

5000

Bound (hours)

Figure 20.2 Cumulative distribution function versus time. Continued



Continued

0.000225 0.000200 0.000175

f(t)

0.000150 0.000125 0.000100 0.000075 0.000050 0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Bound (hours)

Figure 20.3

Part III.E.1

Failure density versus time.

0.0010

0.0008

h(t)

0.0006

0.0004

0.0002

0.0000 0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Bound (hours)

Figure 20.4

Hazard rate versus time.



EXAMPLE 20.2 Suppose the time-to-failure information given is no longer a set of data, but now is well modeled by a particular distribution. For example, suppose the failure time is well estimated or modeled by an exponential distribution with parameter l (see Chapter 34 for details on the exponential distribution). For this distribution the cdf is given by F ( t ) = 1 − e − lt . It can be shown that the pdf (failure density) is given by f ( t ) = l e − lt . Using the relationship that R(t) = 1 – F(t), we also obtain R ( t ) = e − lt . Finally, the hazard rate function can be shown to be

Part III.E.2

h (t ) =

f ( t ) l e − lt = = l. R ( t ) e − lt

This result represents a constant failure rate. The exponential distribution is the only distribution with a constant failure rate function. Section 3 of this chapter discusses this result.

2. SYSTEM RELIABILITY AND MAINTAINABILITY INDICES

Review and analyze indices such as MTTF, MTBF, MTTR, availability, failure rate and so on. (Analyze) Body of Knowledge III.E.2

A product is considered a system when it consists of components that are connected according to some design rules to produce the desired functions of the product. While the previous section discussed how to determine the reliability of individual components, this section covers reliability estimates for systems with a specific focus on simple systems because complex system reliability is sometimes difficult to estimate. In complex systems such as a telecommunications network, the system is composed of units or subsystems connected in a network configuration where the arcs represent the units and the nodes represent connection points along the paths. Reliability estimates of complex systems are often simplified into an aggregation of many simple systems. Methods for estimating reliability of complex systems are given in Elsayed (1996).



This section discusses how to estimate the reliability of two kinds of simple systems, series systems and parallel systems, and addresses k-out-of-n systems and standby systems.

Series Systems A typical series system is composed of n components (or subsystems) connected end-to-end. A failure of any component results in the failure of the entire system. A laser printer, for example, has several major components, such as a photoconductor drum, a laser beam, a toner station, and a paper feed system. The printer fails if any of these components fails. We depict the components graphically, with their respective reliabilities, in a block diagram in Figure 20.5. Under the assumption that each of the component failures are independent, then the reliability of the system is the product of the reliabilities of its components. It is expressed as: Rs ( t ) = R1 ( t ) × R2 ( t ) ...Rn ( t )

(20–8)

EXAMPLE 20.3

Part III.E.2

where Ri (t) is the reliability of the ith component (for i = 1, 2, . . . n). Equation (20–8) assumes that the components are independent, that is, the degradation of one component does not affect the failure rate of other components.

For Figure 20.5, the series reliability R s(t) is computed as follows: R s(t) = 0.96 × 0.92 × 0.94 × 0.90 = 0.7472 The reliability of a series system is lower than its “weakest” component.

Parallel Systems In a parallel system, components or units are connected in parallel so that the failure of one or more paths still allows the remaining path(s) to perform properly. The system fails when all units fail. Under the assumption of independence, the reliability of a parallel system Rs (t) with n units can be estimated by: Rs ( t ) = 1 − ⎡⎣ F1 ( t ) × F2 ( t ) × ... × Fn ( t ) ⎤⎦

(20–9)

Photoconductor drum

Laser beam

Toner station

Paper feed

0.96

0.92

0.94

0.90

Figure 20.5

A typical series system.



or equivalently:

(

) (

)

(

)

Rs ( t ) = 1 − ⎡⎣ 1 − R1 ( t ) × 1 − R2 ( t ) × ... × 1 − Rn ( t ) ⎤⎦

(20–10)

where Fi (t) is the probability of failure of the ith component and Ri (t) is the reliability of the ith component (for i = 1, 2, . . . n). Equation (20–10) results from the relationship given in equation (20–5), that is Fi (t) = 1 – Ri (t). If the components are identical and p is the probability that a component is operational (that is, Ri (t) = p for all units), then the system reliability becomes Rs ( t ) = 1 − ⎡⎣ F1 ( t ) × F2 ( t ) × ... × Fn ( t ) ⎤⎦

(

) (

)

(

)

= 1 − ⎡⎣ 1 − R1 ( t ) × 1 − R2 ( t ) × ... × 1 − Rn ( t ) ⎤⎦ = 1 − ⎡⎣( 1 − p ) × ( 1 − p ) × ... × ( 1 − p ) ⎤⎦

(20–11)

= 1 − (1 − p ) .

Part III.E.2

n

The reliability block diagram of a parallel system is shown in Figure 20.6. The reader is referred to Ebeling (2005) or Tobias and Trindade (1995) for complete discussion of parallel systems. Figures 20.5 and 20.6 show what we refer to as pure series and pure parallel systems, respectively. There are many situations where the design of the system is composed of combinations of series and parallel subsystems, such as parallelseries, series-parallel, and mixed parallel.

EXAMPLE 20.4 For Figure 20.6, the parallel system reliability is computed using equation (20–10) (since the units are not identical): Rs ( t ) = 1 − ⎡⎣( 1 − R1 ( t )) × ( 1 − R2 ( t )) × ... × ( 1 − Rn ( t )) ⎤⎦ = 1 − [( 1 − 0 . 95) × ( 1 − 0 . 93) × ... × ( 1 − 0 . 91)] = 0 . 99 9 69

k-out-of-n Systems Sometimes the system design requires, at a minimum, k-out-of-n functioning units for the system to operate properly. This is a direct application of the binomial distribution, where p represents the probability of success of a component (see Chapter 34 for complete details of the binomial distribution). Assuming the units are identical and independent, the system reliability in this case is given by: n

Rs ( t ) = ∑ n Ci p i ( 1 − p )

n− i

(20–12)

i= k



Unit 1 0.95

Unit 2 0.93

Unit 3 0.91

Figure 20.6

A typical parallel system.

n

Ci =

Part III.E.2

where n! and represents the number of ways i units can be i ! ( n − i )! chosen from a group of n units.

n! = n × (n – 1) × (n – 2) × . . . × 1 0! = 1 See the discussion of the binomial distribution in Chapter 34 for on details on calculating nCi.

EXAMPLE 20.5 Suppose a system has four identical and independent components. System design requirements indicate that a minimum of two components must function for the successful operation of the system. What is the reliability of this system if each component has a reliability of 0.90?

Solution: In this situation, n = 4, k = 2, and p = 0.90. Direct substitution into equation (20–12) yields the following result: 4

Rs ( t ) = ∑ 4C i ( 0 . 90 ) ( 1 − 0 . 90 ) i

4−i

i=2

= 4 C 2 ( 0 . 90 ) ( 1 − 0 . 90 ) + 4C 3 ( 0 . 90 ) ( 1 − 0 . 90 ) + 4C 4 ( 0 . 90 ) ( 1 − 0 . 90 ) 2

2

3

1

4

= 6 ( 0 . 90 ) ( 1 − 0 . 90 ) + 4 ( 0 . 90 ) ( 1 − 0 . 90 ) + 1( 0 . 90 ) ( 1 − 0 . 90 ) 2

2

3

1

4

0

0

= 0 . 9963



Standby Systems Parallel systems are treated as redundant systems. Only one operational path is needed for the system to operate properly. Redundancy can take other forms, such as hot standby redundancy, where all units are operating in parallel at all times. Under this design, all units share the load equally. In standby systems, the standby components function only upon the failure of the main component. The simplest form of a standby system is the one where the components are assumed to be identical, the switch is assumed never to fail, and the standby component is also assumed never to fail while in the standby status. Deviations from these two assumptions present a variety of systems configurations whose analyses go beyond the scope of this section (see Tobias and Trinidad [1995] or Ebeling [2005]). Figure 20.7 shows a standby system with perfect switching (that is the switch will turn on the standby component instantaneously upon the failure of the main component.) Standby system reliability with n standby components is given by n

( lt )

i= 0

i!

Part III.E.2

R ( t ) = e − lt ∑

i

(20–13)

where l is the component failure rate and t the time. If the system has only one standby component, the system reliability is given by R ( t ) = e − lt ⎡⎣1 + lt ⎤⎦ If the system has two standby components, the system reliability is given by R (t ) = e

−lt

2 ⎡ lt ⎤ ⎢1 + lt + ( ) ⎥. ⎢ 2! ⎥ ⎣ ⎦

Main component failure rate

Standby 1 failure rate

Output

Input

Standby n failure rate

Figure 20.7 A standby system with n components in standby mode.



EXAMPLE 20.6 Suppose we have a standby system with three components in standby mode. All components are identical with a constant rate of failure of l = 0.02. What is the system reliability at 75 hours of continuous operation?

Solution: Substitute n = 3, l = 0.02, and t = 75 in equation (20–13): 3

R ( 75) = e −( 0 . 02)(75) ∑

( 0 . 02 × 75)i i!

i=0

3

= e−1.5 ∑

( 1 .55)

i=0

i

i!

⎡ ( 1 . 5 )0 ( 1 . 5 )1 ( 1 . 5 )2 ( 1 . 5 )3 ⎤ = e−1.5 ⎢ + + + ⎥ 1! 2! 3! ⎦ ⎣ 0! = e − 1 . 5 [ 1 + 1 . 5 + 1 . 125 + 0 . 84375] = 0 . 2231[ 4 . 469 ] = 0 . 9970

MTTF =

1 n ∑t n i=1 i

Part III.E.2

Cold standby is another form of redundancy where the minimum number of units needed to operate the system properly share the load equally and other units are available on a standby basis but can only share the load when one or more of the operating units fail. The third type of redundancy is called warm standby. This is similar to the hot standby but not all units share the load equally. Those carrying more than 50 percent of the load are the primary units while the others are considered to be in a warm standby state. When a primary unit fails, the warm standby unit shares the load equally with the remaining primary units. The following paragraphs present some important measures of reliability. The mean time to failure (MTTF) should not be confused with the mean time between failures (MTBF). The expected time between two successive failures is the MTTF when the system is nonrepairable. The expected time between failures, the MTBF, can be calculated when the system is repairable. First consider n identical nonrepairable systems and observe the times to failure for them. Assume that the observed times to failure are t1, t2, . . ., tn. The estimated mean time to failure MTTF is: (20–14)

For constant failure rate the mean time to failure is: MTTF =

1 l

which can be interpreted as the reciprocal of the failure rate. It should be noted that this is only true for the constant failure rate model. The accurate method for



estimating the mean time to failure for discrete time intervals is given in equation (20–14). It can be estimated by using integration for continuous time functions.

Maintainability and Availability We have presented several measures of reliability for nonrepairable systems that include reliability function and mean time to failure. Other measures of reliability are defined for repairable systems, such as system availability (instantaneous, average up-time, inherent, operational, and achieved availabilities), mean time to repair, and maintainability. Common to all these definitions is that the system is subject to repair or replacement upon failure. Availability at time t is defined as the probability that the system is properly operating at that time. The steady state availability is the long-term availability of the system (t → ∞). The steady state availability A is defined as

Part III.E.2

A=

MTBF MTBF + MTTR

where MTBF and MTTR are the mean time between failures and mean time to repair respectively. MTTR is defined as the average time to repair a failure, not including waiting time for parts or tools to start the repair. Maintainability is defined as the probability that a failed system is restored to its operational condition within a specified time. Maintenance actions or policies can be classified as corrective maintenance, preventive maintenance, and predictive maintenance (which is also called on-condition maintenance). Maintenance actions are dependent on many factors, such as the failure rate of the machine, the cost associated with downtime, the cost of repair, and the expected life of the machine. A corrective maintenance policy requires no repairs, replacements, or preventive maintenance until failures occur, which allows for maximum run time between repairs. Although a corrective maintenance policy does allow for maximum run time between repairs, it is neither economical nor efficient, as it may result in a catastrophic failure that requires extensive repair time and cost. A preventive maintenance policy requires maintaining a machine according to a predetermined schedule, whether a problem is apparent or not. On a scheduled basis, machines are removed from operation, disassembled, inspected for defective parts, and repaired accordingly. Actual repair costs can be reduced in this manner, but production loss may increase if the machine is complex and requires days or even weeks to maintain. Preventive maintenance also may create machine problems where none existed before. It is important to note that preventive maintenance is only applicable when the following conditions are satisfied: 1. The cost to repair the system after its failure is greater than the cost of maintaining the system before its failure. 2. The failure rate function of the system is monotonically increasing with time. Clearly, if the system’s failure rate is decreasing with time, then the system is likely to improve with time and any preventive action



or replacement is considered a waste of resources. Likewise, performing preventive maintenance when the failure rate is constant is improper, as replacing or maintaining the system before failures does not affect the probability that the system will fail in the next instant, given that it is now good (Jardine and Buzacott (1983).

Part III.E.3

The third repair policy is the predictive maintenance policy. Obviously, tremendous savings can result if a machine failure can be predicted and the machine can be taken off-line to make only the necessary repairs. Predictive maintenance can also be done when failure modes for the machine can be identified and monitored for increased intensity, and when the machine can be shut down at a fixed control limit before critical fault levels are reached. Predictive maintenance results in two benefits. The first benefit is the result of taking a machine off-line at a predetermined time, which allows production loss to be minimized by scheduling production around the downtime. Since defective components can be predetermined, repair parts can be ordered and manpower scheduled for the maintenance accordingly. Moreover, sensors for monitoring the machines eliminate time spent on diagnostics, thus reducing the time to perform the actual repair. The second benefit is that only defective parts need to be repaired or replaced and the components in good working order are left as is, thus minimizing repair costs and downtime. Three main tasks must be fulfilled for predictive maintenance. The first task is to find the condition parameter that can describe the condition of the machine. A condition parameter could be any characteristic, such as vibration, sound, temperature, corrosion, crack growth, wear, or lubricant condition. The second task is to monitor the condition parameter and to assess the current machine condition from the measured data. The final task is to determine the limit value of the condition parameter and its two components, the alarm value and the breakdown value. A running machine reaching the alarm value is an indication that the machine is experiencing intensive wear. At this point, the type and advancement of the fault must be identified in order to prepare the maintenance procedure. If a machine reaches the breakdown value, the machine must be shut down for maintenance. See Ebeling (2005) for detailed discussion of availability and maintainability.

3. THE BATHTUB CURVE AND OTHER FAILURE MODELS Identify, define, and distinguish between the basic elements of the bathtub curve. (Analyze) Body of Knowledge III.E.3

One of the earliest models of failure rate, the bathtub curve (see Figure 20.8), is so named because of its shape. The failure rate versus time can be divided into three regions. The first region is characterized by a decreasing failure rate with time and



Hazard rate

152 Part III: Product and Process Design

Early life region

Constant failure rate region

T1

Part III.E.3

Figure 20.8

Wear-out region

T2

Time

The general failure rate model (the bathtub curve).

is conventionally referred to as the infant mortality phase or the early life region of the product, component, or system during their early period of use. Experience shows that the length (0 to T1) of this region is about 10,000 hours (approximately one year) for most electronic components. The failures in this region are usually attributed to defects in the manufacturing processes, assemblies, and shipping of the product. The second region of the bathtub curve is the constant failure rate region, which is characterized by the inherent failure rate of the product’s composite components. In this region, the failures occur randomly over time, as shown in Example 1. The third region is referred to as the wear-out region. It is characterized by an increasing failure rate over time. Most electronic components do not exhibit such a region, with the exception of electro-mechanical devices, such as relays. On the other hand, most, if not all, mechanical components that are subjected to rotating and alternating motions wear out with time. This is exemplified by the behavior of cutting tools, fatigue loading on structures, and wear-out due to friction between mating surfaces. In Example 1, we showed a case of constant failure rate (we use hazard rate and failure rate interchangeably). This is the simplest failure model, as its probability density function and reliability function can easily be shown in the following section, whereas other failure rate models (decreasing or increasing) are sometimes difficult to obtain from their corresponding functions. The second region in the general failure rate model (bathtub curve) shows constant failure rate. Let l be the constant failure rate. Thus: h (t ) = l The reliability function and the probability density function are given in Equations (20–15) and (20 –16) respectively: R ( t ) = e − lt

(20–15)

f ( t ) = h ( t ) R ( t ) = l e − lt

(20–16)



This is the standard exponential failure time distribution. The graphs of Equations (20–15) and (20–16), shown in Figures 20.9 and 20.10, are similar to those in Figures 20.2 and 20.3, which are obtained from actual failure data.

Probability density function

0.1

0.08

0.06

0.04

0.02

0

10

20

30

40

50

60

Time

Figure 20.9

Part III.E.3

0

Probability density function for constant failure rate.

1

Reliability

0.8

0.6

0.4

0.2

0 0

10

20

30

40

50

60

Time

Figure 20.10

Reliability function for constant failure rate.



The first and third regions (the decreasing and increasing failure rate regions) of the general failure rate models can be described by time-dependent failure rate functions. The Weibull failure rate is the most widely used failure rate model that describes these regions. It is expressed as: h (t ) =

g g −1 t q

(20–17)

Part III.E.3

where g and q are the shape and scale parameters of the two-parameter Weibull distribution. For discussion of the Weibull distribution, see chapter 34. The appeal of the Weibull hazard rate function comes from the fact that it can represent several other known functions. For example, when g = 1 the Weibull hazard rate function becomes constant. When g = 2, the resultant hazard function is linear with time and its probability density function becomes the Rayleigh distribution. Indeed, Makino (1984) shows that the normal distribution can be approximated to Weibull when g = 3.43927. The reliability function and the probability density function of the Weibull distribution are expressed respectively as: R (t ) = e

− tg q

t>0

(20–18)

and f (t ) =

g

g g −1 −qt t e q

(20–19)

Figures 20.11 and 20.12 demonstrate the use of the Weibull failure model to describe decreasing and increasing failure rates. Of course, the constant failure rate is also included. Other probability distributions can be used to appropriately describe the failure times, including: gamma, beta, log-logistics, lognormal, extreme value, and normal distributions (Elsayed 1996).

Reliability Failure Analysis and Reporting In order to be effective, a comprehensive reliability program must be based on data that is collected, verified and/or validated, analyzed, and used as the basis of decision making for design improvements and corrective action. At a minimum, reliability data must be thoroughly evaluated at key milestones such as design phase and program reviews. In the context of failure analysis and reporting, reliability data is most commonly evaluated in a closed-loop failure reporting and corrective action system. For purposes of this chapter, a closed-loop failure reporting and corrective action system provides the means to ensure that failures are not only documented and tracked over time, but also analyzed to a sufficient depth to determine whether corrective action is required, and if so, what corrective action is necessary as determined by appropriate design engineers or a reliability review board.



1.5

Shape, scale

Probability density function

1.2

2,1 3,1 0.9 0.7, 1

0.6

0.3

0 2

0

4

6

8

10

12

Time

Part III.E.3

Figure 20.11 Probability density functions for the Weibull model with different shape and scale paramenters.

6

5

Shape, scale

Hazard rate function

2,1 4

3,1 0.7, 1

3

2

1

0 0

2

4

6

8

10

12

Time

Figure 20.12

Hazard rate functions for the Weibull model with different shape and scale paramenters.



4. RELIABILITY, SAFETY, AND HAZARD ASSESSMENT TOOLS Define, construct, and interpret the results of failure mode and effects analysis (FMEA). failure mode, effects, and criticality analysis (FMECA), and fault tree analysis (FTA). (Analyze)

Part III.E.4

Body of Knowledge III.E.4

During the design phase of the system, and when the system fails during operation, it is important to determine potential failures and their causes to eliminate critical failures (those that cause total interruption of the function or potential injuries to users) by identifying the causes of failures and by developing appropriate methods to reduce their effects. Several approaches that have proven to be effective in identifying potential failures are discussed here: environmental stress screening (ESS), failure mode and effects analysis (FMEA), failure mode effects and criticality analysis (FMECA), and fault tree analysis (FTA).

Environmental Stress Screening Environmental stress screening (ESS) is a process designed to precipitate incipient defects into detectable failures by use of environmental stresses applied to hardware. ESS is most efficient when used at the lowest practical level of hardware. When used at the part level, ESS is often called burn-in. The most frequently used environments for ESS are temperature cycling and random vibration. Other environments, such as shock, altitude, humidity, and so on, can be used based on the product type and its intended use conditions. Experience shows that the sequence of the application of environments has been found to play a minor role in the effectiveness of ESS. The following two conditions are necessary when applying ESS: 1. The product’s design limit should not be exceeded. 2. More severe environments should be applied at the lower levels of the hardware so that screening environments become less severe with increasing levels of hardware complexity. This will cause failures at the lower levels where it is less costly to replace or repair. The Environmental Stress Screening of Electronic Hardware (ESSEH) committee of the Institute of Environmental Sciences has compiled data from throughout the electronics industry and made the following recommendations: • The optimal number of thermal cycles for electronics is 10. This number is obtained based on experience; scientific or mathematical methods are needed to obtain the “true” optimum for given test conditions and constraints.



• Random vibration is a more efficient screen than sinusoidal vibration. • The preferred random vibration profile is the naval material command (NAVMAT) profile, which covers the frequency spectrum from 20 hertz to 2000 hertz with an overall acceleration of six grams. Studies show that the vast majority of failures with this profile occur in the first 10 minutes of the test. It is important that a baseline experiment be conducted and analyzed during ESS to determine the optimum screening parameters.

Failure Mode and Effects Analysis

Part III.E.4

Failure mode and effects analysis (FMEA) is a team-based problem-solving tool intended to help users identify and eliminate or reduce the negative effects of potential failures before they occur in systems, subsystems, product or process design, or the delivery of a service. FMEA can be used as a stand-alone tool or as part of comprehensive quality programs such as ISO 9000, QS-9000, advanced product quality planning and control plan (APQP), or Six Sigma. Accordingly, this section discusses terminology, theory, mechanics, and applications of FMEA as it applies to product designs, process designs, and systems. A word of caution: FMEA can be a powerful and effective tool for system, subsystem, product or process design, or service delivery improvement, but completing an FMEA has significant costs associated with it. Organizations that may be tempted to follow the results of an FMEA to implement further levels of refinement and specificity should conduct a cost/benefit analysis to ensure that FMEA is the proper tool under the circumstances.

Selecting a Standard for FMEA There are two primary standards for FMEA, the military standard (MIL-STD 1629A) and the Society of Automotive Engineers standard (SAE J1739). Both standards are limited in scope to address only design and process FMEAs. These standards provide general FMEA forms and documents, identify criteria for the quantification of risk associated with potential failures, and provide very general guidelines on the mechanics of completing FMEAs. MIL-STD 1629A and SAE J1739 may be obtained by contacting the Department of the Navy and the Society of Automotive Engineers at www.navy.mil or www.sae.org/technical/standards, respectively. Another useful reference is the manual Potential Failure Effects Analysis published by the Automotive Industry Action Group (AIAG). This manual is available at http://www.aiag.org. Failure mode and effects analysis (FMEA) can also be implemented in other fields, such as healthcare. The Joint Commission recommends several resources and manuals on FMEA in healthcare which can be found at http://store.jcrinc. com/JCRStore. The interested reader is encouraged to visit the Joint Commission at http://www.jointcommission.org or the Institute for Healthcare Improvement (IHI) at http://www.ihi.org for more information about FMEA and healthcare research and accreditation.



Planning for an FMEA Planning for an FMEA involves a series of considerations that include, as a minimum, the following: • Select appropriate applications for the analysis. An FMEA may be authorized by individuals at various levels within an organization or may be required by ISO 9000, QS-9000, APQP, Six Sigma methodologies, internal quality programs, or customer requirements. However authorized or required, an FMEA is expensive to complete and should be completed only in those instances where the benefits outweigh the costs. • Identify and allocate resources. These resources include FMEA team members and a reporting structure, physical space to conduct the analysis and store documentation, time, and clerical/communications support.

Part III.E.4

• Define the scope. Since an FMEA can be conducted at a high level (that is, the system level) or at a very detailed level (that is, the component level or service delivery level), and since a high-level FMEA may lead to additional FMEAs at more detailed levels, it is very important to set the scope of the analysis before beginning. • Establish expectations and deliverables. The team-based nature of completing an FMEA means FMEA team members will have dual or multiple responsibilities and reporting structures in addition to the FMEA team. It is critical, therefore, to clearly define performance expectations for all FMEA team members and to communicate those expectations directly to appropriate supervisory or managerial personnel in reporting structures outside the FMEA team. It is equally important that all FMEA team members understand what deliverables will result from the analysis and their respective roles in developing those deliverables. • Establish milestones, due dates, and deadlines. Key milestones for an FMEA include authorization for the analysis, establishment of a reporting structure, allocation of resources (particularly FMEA team members), gathering input for the analysis, completing the analysis, taking and monitoring corrective action, preparing documentation, and report-outs and debriefings. To ensure effectiveness, an FMEA should be conducted like a project from the perspective of establishing a schedule specifying due dates and deadlines for each of the major milestones. • Establish a single point of responsibility. Although FMEA is a team-based analysis, sufficient practical experience supports the idea that assigning responsibility to a cross-functional team rather than a single individual is not the most effective policy. So for a variety of reasons, a single person should be assigned the responsibility of FMEA team leader, and that person needs the authority to make decisions and allocate resources to complete the FMEA as planned.

FMEA Team Members The belief that only the one or two people closest to a system, subsystem, product or process design, or service delivery should be assigned to an FMEA violates the very intent of the analysis. FMEA is intended to be completed by team members



representing a broad cross section of expertise—technical and nontechnical. For example, an FMEA team should have representation from the following functional groups, as a minimum: • Design engineering • Manufacturing engineering • Production • Quality/reliability • Purchasing/material control • Sales and marketing • Customers It cannot be overemphasized that for an FMEA to be truly effective, the viewpoints and perspectives of every functional group mentioned above must be included— particularly customers. As Palady (1997) explains, “excluding the customer’s input from the FMEA will result in an incomplete list of the effects and low estimates of the severity.”

To prepare for an FMEA, it is necessary to gather information from several sources—and these data should be gathered prior to the initial FMEA team meeting to maximize the effectiveness of team members’ time. Minimum inputs to an FMEA include:

Part III.E.4

Inputs to an FMEA

• Process flowchart or functional block diagram • Design specifications • Customer requirements/specifications • Testing data/results • Data on similar process/design technology • Warranty data • Failure/rework data • Design/configuration change data • Prior FMEAs • Results from quantitative analysis (DOE, SPC, process capability assessments, reliability assessments, and so on)

FMEA and Other Quality Tools In addition to the inputs described above, other quality tools are frequently used during the completion of an FMEA. These other quality tools include, but are not limited to, the following:



• Cause-and-effect diagrams • Process decision program charts • Histograms • Pareto diagrams • Run charts • Force-field analysis • Fault tree diagrams • Root cause analysis

Outputs from an FMEA Outputs or deliverables from an FMEA include the following: • FMEA documentation

Part III.E.4

• System, subsystem, design, process, and/or service delivery documentation • Recommendation reports • Corrective action reports • Design changes • Compliance reports • Debriefings and presentations

Basic Steps in an FMEA Complexity in an FMEA is directly related to the number of levels of analysis dictated by the situation or team members. At the most fundamental level, however, every FMEA consists of the same basic steps, including: 1. Identify a starting point for the analysis. A starting point will be a system, subsystem, product or process design, or service delivery system of interest. 2. Gather all relevant inputs to support the analysis. Gathering inputs for an FMEA is a milestone to be completed prior to the initial FMEA meeting. It is far more effective, both from cost and efficiency perspectives, to have all team members at meetings participating in the analysis rather than leaving meetings to gather input! 3. Identify potential failure modes such as: • Who would be impacted by a failure? • What would happen in the event of a failure? • When would the failure occur?



• Where would the failure occur? • Why would the failure occur? • How would the failure occur? 4. Quantify the risk associated with each potential failure. Risk assessment is based on severity, occurrence, and detection of a potential failure. 5. Develop a corrective action plan for the most significant risks. 6. Repeat the analysis until all potential failures pose an acceptable level of risk. What constitutes an acceptable risk must be clearly defined by the individual or agent authorizing the FMEA. 7. Document results. 8. Report-out and/or present results.

Obtaining Risk Priority Numbers

Severity (S)—An indicator of the severity of a failure should a failure occur. Severity is described on a 10-point scale. Occurrence (O)—An indicator of the likelihood of a failure occurring. Occurrence is described on a 10-point scale.

Part III.E.4

For purposes of the FMEA, risk has three components: severity, occurrence, and detection. Each of these components is assigned a value, and the values are multiplied to produce risk priority numbers (RPN).

Detection (D)—An indicator of the likelihood of detecting a failure once it has occurred. Detection is described on a 10-point scale. RPNmin = 1 while RPNmax = 1000

Taking Action Based on an RPN A common mistake in assessing FMEA risk is prioritizing corrective action based on the descending order of RPNs. Logic would suggest that the largest RPNs represent the highest risk—which is true, but only to a point. When multiplying the three risk components together, their importance relative to each other becomes obscured. Consider the following example: (S)

*

(O)

Potential failure 1

2

10

Potential failure 2

10

Potential failure 3

2

Potential failure 4

10

*

(D)

=

RPN

5

100

2

5

100

5

10

100

5

2

100

In each case the resulting RPN = 100, so it is unclear what potential failure to take corrective action on first. There is, however, a generally accepted strategy when taking action on an RPN, and Palady (1997) describes that strategy as follows:



1. Eliminate the occurrence 2. Reduce the severity 3. Reduce the occurrence 4. Improve detection Applying this strategy shows how to proceed: Eliminating occurrences would, mathematically, reorder the RPNs. Reducing severity next would focus our attention on potential failures 2 and 4. But then what? We still have two potential failures with the same level of risk.

Part III.E.4

Reducing occurrence as the next step in this process focuses our attention on potential failure 4, which had a higher occurrence rating than did potential failure 2. Now our attention can turn to evaluating the remaining potential failures since potential failures 2 and 4 have been ranked as the two most important. Of the remaining two potential failures, potential failure 1 has the higher occurrence rating and is therefore ranked as the third most important potential failure, and potential failure 3 drops to the least important position by default. The rank order by which the potential failures in the above example should be investigated for corrective action is as follows: First priority

Potential failure 4

Second priority

Potential failure 2

Third priority

Potential failure 1

Fourth priority

Potential failure 3

Do We Rate the Failure Mode or the Cause? A common point of confusion arises when considering what is actually rated as part of the risk assessment—the actual failure itself or the cause of a given failure. It is perfectly acceptable to rate either the failure or the cause, as long as the assumption is well-documented (on actual FMEA charts, in written correspondence, and in all reports/presentations) and everyone on the FMEA team and in the reporting structure is aware of the assumption. Whether rating a failure itself or a cause of that failure, an FMEA should provide consistent results and corrective actions.

FMEAs Encountered by Quality Engineers FMEA can be applied to the system, subsystem, design or process, or service delivery levels. A brief synopsis of each FMEA application is as follows:



• System FMEA. A system, or subsystem, is a collection of elements or components working together to accomplish a desired task or function. FMEA is applied at the system or subsystem level to identify potential failure modes and effects that could negatively impact system or subsystem performance. At the system or subsystem level, FMEA is focused at system or subsystem boundaries where potential failures are most likely to occur. The boundaries of interest for a system or subsystem FMEA include functional (that is, expected outcomes assuming normal operation) or operational (that is, specific outputs expected as compared to tolerances, specifications, and timing). • Design FMEA. A design, or more accurately a product design, is a set of specifications that describes all aspects of a product (that is, major functions, operating parameters and tolerances, materials, dimensions, and so on). FMEA is applied to product designs as early in the product design process as is feasible to identify potential failure modes that could result from a design flaw. Design FMEAs are a normal part of key milestones in the product development process, such as concept reviews, concept approvals, preliminary design reviews, and final design reviews.

Part III.E.4

• Process FMEA. A process design is a set of specifications that describes all aspects of a process (that is, functional components, flow rates, process steps, equipment to be used, steps to be performed, operators or employees to be involved, and so on). Process design FMEA is applied to process designs at the earliest possible point to identify potential failure modes that could result from a design flaw. Process FMEAs, also, are a normal part of key milestones in the process development process. • Service delivery FMEA. A service delivery is the completion of a set of tasks designed to meet one or more customer expectations. Service delivery FMEA is applied to service delivery designs to identify potential failure modes that, if experienced, would result in some level of dissatisfaction from customers. Service delivery FMEAs are also completed as early as possible in the design process and are a normal part of key milestones in the service delivery design process. In most instances, the practicing quality engineer (QE) can be expected to work primarily on design and/or process FMEAs. Accordingly, this chapter will focus on design and process FMEAs, and will omit system/subsystem and service delivery FMEAs. Readers are encouraged to reference Stamatis (2003) for a detailed discussion of system/subsystem and service delivery FMEAs.

Design and Process FMEAs Following the steps previously outlined that described the planning functions preceding an FMEA, the analysis proceeds as the FMEA team completes appropriate documentation, such as the FMEA form. For purposes of this discussion, one form applicable to either a design or process FMEA will be described. Where the criteria change between a design or process FMEA, both criteria will be provided. Figures 20.13 and 20.14 are blank FMEA forms applicable to design and process FMEA—each component of the forms will be identified and described.


Figure 20.13 Blank design FMEA form.

Function

C S l e a v s s

Potential effect(s) of failure

Item

Potential failure mode

Key date

Potential cause(s)/ mechanism(s) of failure

Design responsibility

Model year(s) vehicle(s) Core team

System Subsystem Component

O c c u r Current design controls

of

FMEA date (orig.)

Prepared by

Page

FMEA number

D Responsibility e R. and target t P. Recommended action(s) completion e N. date c

Potential Failure Mode and Effects Analysis (Design FMEA)

Part III.E.4

Actions taken

S O D R. e c e P. v c t N.

Action results

(rev.)



Figure 20.14

Blank process FMEA form.

Requirements

Potential cause(s)/ mechanism(s) of failure

C S l e a v s s

Process function Potential effect(s) of failure

Key date

Model year(s) vehicle(s) Core team


Process responsibility

O c c u r Current process controls

Part III.E.4

Item

of

FMEA date (orig.)

Prepared by

Page

FMEA number

D Responsibility e R. and target t P. Recommended action(s) completion e N. date c

Potential Failure Mode and Effects Analysis (Process FMEA)

Actions taken


Action results

(rev.)




Part III.E.4

Heading Information and Documentation Product or process name

Provide the formal and/or commonly used (if different) name for the product or process.

Product or process description

Provide a brief description of the product or process that is meaningful to the FMEA team members.

FMEA number

Assign an FMEA number to each FMEA for tracking and documentation purposes. There are no standards for numbering FMEAs; however, a numbering system that links the FMEA to a specific period of time and product/process family is preferred.

Design/process owner

Identify the individual or team assigned primary responsibility for the design or process for tracking and documentation purposes. This individual or team is also identified for reference, if needed, during the FMEA.

FMEA team leader

Identify the individual assigned primary responsibility for completion of the FMEA for documentation purposes. This individual is also identified so as to establish a point of contact should any stakeholder need information during or after the FMEA.

FMEA team

List each member of the FMEA team along with any key responsibilities relative to the FMEA.

FMEA date

Provide the date(s) during which the FMEA is completed to help establish a chronology of events. Revision dates should be noted here as well.

FMEA risk assessment

Indicate the basis of the risk assessment. The FMEA risk assessment may be based on either actual failures or failure causes. It is important to document the team’s decision to assess risk based on failures or causes to ensure that everyone evaluating the FMEA understands exactly how risk was assessed.

Analysis Content and Documentation DFMEA part name, number, function, or PFMEA process function

Identify the product (that is, part name, part number, and function) or process (that is, functions to be completed as part of the process).




List each of the potential failure modes associated with the design or process. Design failure modes may include dented, deformed, fractured, loosened, leaking, warped, and so on. Process failure modes may include overheating, inoperable, visual defect, and so on.

Potential effect of failure mode

For each potential failure mode, indicate the potential effect on customers or production/ process personnel—it is entirely possible to have multiple effects for each potential failure mode.

Severity

Indicate the seriousness of the effect of the potential failure using the severity criteria defined in Tables 20.3 and 20.4. Note: The severity rating applies only to the effect of the potential failure.

Table 20.3

Design FMEA severity criteria. Severity criteria

Ranking

Hazardous without warning

Very high ranking when potential failure mode affects safe operation and/or regulation noncompliance. Failure occurs without warning.

10

Hazardous with warning

Very high ranking when potential failure mode affects safe operation and/or regulation noncompliance. Failure occurs with warning.

9

Very high

Item or product is inoperable, with loss of function. Customer very dissatisfied.

8

High

Item or product is operable, with loss of performance. Customer dissatisfied.

7

Moderate

Item or product is operable, but comfort/convenience items inoperable. Customer experiences discomfort.

6

Low

Item or product is operable, but with loss of performance of comfort/convenience items. Customer has some dissatisfaction.

5

Very low

Certain characteristics do not conform. Noticed by most customers.

4

Minor

Certain characteristics do not conform. Noticed by average customers.

3

Very minor

Certain characteristics do not conform. Noticed by discriminating customers.

2

None

No effect.

1

Part III.E.4

Effect

S × O × D = risk priority number (RPN) Derived from Technical Standard SAE J 1739. Reprinted by permission of The Society of Automotive Engineers (SAE).



Part III.E.4

Table 20.4 Process FMEA severity criteria. Effect

Severity criteria

Ranking

Hazardous without warning

May endanger machine or assembly operator. Very high severity ranking when a potential failure mode affects safe operation and/ or involves noncompliance with regulation. Failure will occur without warning.

10

Hazardous with warning

May endanger machine or assembly operator. Very high severity ranking when a potential failure mode affects safe operation and/ or involves noncompliance with regulation. Failure will occur with warning.

9

Very high

Major disruption to production line. 100% of product may have to be scrapped. Item inoperable, loss of primary function. Customer very dissatisfied.

8

High

Minor disruption to production line. A portion of product may have to be sorted and scrapped. Item operable, but at reduced level. Customer dissatisfied.

7

Moderate

Minor disruption to production line. A portion of product may have to be scrapped (no sorting). Item operable, but some comfort items inoperable. Customer experiences discomfort.

6

Low

Minor disruption to production line. 100% of product may have to be reworked. Item operable, but some comfort items operable at reduced level of performance. Customer experiences some dissatisfaction.

5

Very low

Minor disruption to production line. Product may have to be sorted and a portion reworked. Minor adjustments do not conform. Defect noticed by customer.

4

Minor

Minor disruption to production line. Product may have to be reworked online, but out of station. Minor adjustments do not conform. Defect noticed by average customer.

3

Very minor

Minor disruption to production line. Product may have to be reworked online, but out of station. Minor adjustments do not conform. Defect noticed by discriminating customer.

2

None

No effect.

1

Derived from Technical Standard SAE J 1739. Reprinted by permission of The Society of Automotive Engineers (SAE).

Classification

Classify any special characteristics that may require additional process controls. SAE J1739 identifies classifications that include critical, key, major, and significant.

Potential cause of failure mode

For each potential effect of each failure mode, identify all possible causes—it is entirely possible to have more than one cause for each potential effect.



Table 20.5

Design FMEA occurrence criteria.

Probability of failure

Possible failure rates

Ranking

Very high: Failure almost inevitable

> 1 in 2 1 in 3

10 9

High: Repeated failures

1 in 8 1 in 20

8 7

Moderate: Occasional failures

1 in 80 1 in 400 1 in 2000

6 5 4

Low: Relatively few failures

1 in 15,000 1 in 150,000

3 2

Remote: Failure is unlikely

< 1 in 1,500,000

1


Process FMEA occurrence criteria.

Probability of failure

Possible failure rates

Ranking

Very high: Failure almost inevitable.

> 1 in 2 1 in 3

10 9

High: Generally associated with processes similar to previous processes that have often failed.

1 in 8 1 in 20

8 7

Moderate: Generally associated with processes similar to previous processes that have experienced occasional failures.

1 in 80 1 in 400 1 in 2000

6 5 4

Low: Isolated failures associated with similar processes.

1 in 15,000

3

Very low: Only isolated failures associated with almost identical processes.

1 in 150,000

2

Remote: Failure is unlikely. No failures associated with almost identical processes.

< 1 in 1,500,000

1

Part III.E.4

Table 20.6


Occurrence

Indicate how frequently each failure is expected to occur using the occurrence criteria defined in Tables 20.5 and 20.6.

DFMEA design verifications or PFMEA process controls

For a design FMEA, identify the actions completed that ensure or verify the adequacy of the design. For a current process FMEA, identify the control currently in place that prevents a failure mode from occurring.



Part III.E.4

Table 20.7

Design FMEA detection criteria.

Effect

Detection criteria

Ranking

Absolute uncertainty

Design control will not and/or cannot detect a potential cause/ mechanism and subsequent failure mode or there is no design control.

Very remote

Very remote chance the design control will detect a potential cause/mechanism and subsequent failure mode.

9

Remote

Remote chance the design control will detect a potential cause/ mechanism and subsequent failure mode.

8

Very low

Very low chance the design control will detect a potential cause/ mechanism and subsequent failure mode.

7

Low

Low chance the design control will detect a potential cause/ mechanism and subsequent failure mode.

6

Moderate

Moderate chance the design control will detect a potential cause/mechanism and subsequent failure mode.

5

Moderately high

Moderately high chance the design control will detect a potential cause/mechanism and subsequent failure mode.

4

High

High chance the design control will detect a potential cause/ mechanism and subsequent failure mode.

3

Very high

Very high chance the design control will detect a potential cause/mechanism and subsequent failure mode.

2

Almost certain

Design control will almost certainly detect a potential cause/ mechanism and subsequent failure mode.

1

10


Detection

Indicate the ability of design verification or current process controls to detect a potential failure mode in the event that failure actually occurs. Use the detection criteria defined in Tables 20.7 and 20.8.

Risk priority number (RPN)

For each potential failure mode, multiply the severity (S), occurrence (O), and detection (D) assessments together. Since each scale (S, O, and D) ranges from 1 to 10, RPNmin = 1 and RPNmax = 1000.

Recommended actions

For each potential failure mode, list one or more recommended corrective actions. For further direction and guidance on prioritizing recommended corrective actions, refer to the “Taking Action Based on an RPN” section of this chapter.



Table 20.8 Process FMEA detection criteria. Effect

Detection criteria

Ranking

Absolutely impossible

No known controls to detect failure mode.

Very remote

Very remote likelihood current controls will detect failure mode.

9

Remote

Remote likelihood current controls will detect failure mode.

8

Very low

Very low likelihood current controls will detect failure mode.

7

Low

Low likelihood current controls will detect failure mode.

6

Moderate

Moderate likelihood current controls will detect failure mode.

5

Moderately high

Moderately high likelihood current controls will detect failure mode.

4

High

High likelihood current controls will detect failure mode.

3

Very high

Very high likelihood current controls will detect failure mode.

2

Almost certain

Current controls will almost certainly detect a failure mode. Reliable detection controls are known with similar processes.

1

10

Individual/team responsible and completion date

For each recommended action, assign an appropriate individual or team and an expected completion date.

Actions taken

Provide a brief description of the actual actions taken and their respective action dates.

Resulting RPN analysis

Following each action taken, reiterate the severity, occurrence, and detection assessments and calculate a new resulting RPN. Actions taken based on RPNs and resulting RPNs continue until the risk assessment for each potential failure is “acceptable” to the customer and/or authorizing agent for the FMEA.

Part III.E.4


A Final Word on Taking Corrective Action An FMEA represents an in-depth, objective, quantitative analysis of the risk associated with potential failures that result in the calculation of one or more RPNs. Once RPNs have been calculated and the FMEA team prepares to take corrective action, the analysis necessarily takes on a subjective element as FMEA team members use the risk assessment to guide prioritization of corrective actions. As was mentioned earlier in this chapter, the most common practice is to prioritize corrective action based on RPNs. Prioritizing corrective action based solely



on RPNs works effectively, however, only as long as there is a “comfortable” difference among the RPN values. When there are clusters of RPN values that are the same, or very close (that is, within 25 to 50 points), taking action based on RPNs alone is not straightforward. When there are clusters of RPN values (that is, grouping of RPN values that are the same or within a 25 to 50 point range), follow these steps to prioritize corrective action: 1. Rank the RPNs in descending order. 2. For those RPNs that cluster within a predefined range, for example, 25 to 50 points, eliminate occurrence, then reduce severity, then reduce occurrence, then improve detection. 3. Plan, take, and monitor corrective action on the largest nonclustered RPNs.

Part III.E.4

4. Plan, take, and monitor corrective action on RPN clusters as defined in step 2. Repeat steps 3 and 4 as needed to address all potential failures identified in the analysis. As another means of eliminating the subjectivity in prioritizing corrective actions based on RPNs, a method called criticality analysis was developed as part of MIL-STD 1629A.

Design and Process FMEA Examples Figures 20.15 and 20.16, showing examples of design and process FMEAs, have been provided to help guide the reader through an actual analysis.

Failure Mode Effects and Criticality Analysis (FMECA) MIL-STD 1629A defines two very important terms and concepts with respect to risk assessment: Criticality

“A relative measure of the consequences of a failure mode and its frequency of occurrences.”

Criticality Analysis

“A procedure by which each potential failure mode is ranked according to the combined influence of severity and probability of occurrence” (MIL-STD-1629A).

When criticality is considered in an FMEA, the name is changed to failure mode effects and criticality analysis (FMECA). FMECA can be a qualitative or quantitative assessment of risk that leads to a prioritization of corrective action based on severity (S) and occurrence (O) assessments. In the qualitative approach to risk assessment in FMECA, risk is categorized as frequent, reasonably probable, occasional, remote, or extremely unlikely. In the quantitative approach to risk assessment in FMECA, failure rate data, failure effect probability data, individual part failure data, and operating time data are required as input to one or more protocols as defined in Military Handbook 217.


9

12

• Unsatisfactory appearance due to rust through paint over time • Impaired function of interior door hardware

Deteriorated life of door leading to:

11

10

Corroded interior lower door panels



Design FMEA example.

• Ingress to and egress from vehicle • Occupant protection from weather, noise, and side impact • Support anchorage for door hardware including mirror, hinges, latch, and window regulator • Provide proper surface for appearance items • Paint and soft trim

Front door L.H. H8HX-0000-A

Function

Item

Model year(s)/vehicle(s) 199X/Lion 4dr/wagon

5

2

Design responsibility Body engineering

7

C S l e a v s s

4

2

5

3

4

Inappropriate wax formulation specified

Entrapped air prevents wax from entering corner/edge access

Wax application plugs door drain holes

Insufficient room between panels for spray head access

6

O c c u r

Insufficient wax thickness specified

Upper edge of protective wax application specified for inner door panels is too low

14

cause(s)/ mechanism(s) of failure

13 Potential

16

Current design controls

17

SAMPLE

Drawing evaluation of spray head access

Laboratory test using "worst case" wax application and hole size

Design aid investigation with non-functioning spray head

Physical and Chem Lab test - Report No. 1265

Vehicle general durability testing - as above

Vehicle general durability test vah. T-118 T-109 T-301

15

Part III.E.4

Figure 20.15

System Subsystem Component 01.03/Body closures 3

of

1234

Combine w/test for wax upper edge verification

7 196 Add laboratory accelerated corrosion testing

21 None

4 112 Add team evaluation using design aid buck and spray head

1

8 280 Add team evaluation using production spray equipment and specified wax

28 None

Body Engrg & Assy Ops

Body Engrg & Assy Ops 8X 11 15

A Tate Body Engrg 9X 01 15

A Tate-Body Engrg 8X 09 30

2

1

4

7

Evaluation showed adequate access

Based on test, 3 additional vent holes provided in affected areas 7

7

Test results 7 (Test No. 1481) show specified thickness is adequate. DOE shows 25% variation in specified thickness is acceptable

Based on test results (Test No. 1481) upper edge spec raised 125mm

1

1

2

2

1

3

2

2

7

21

28

28


Action results 22 Actions taken

21

8

7 FMEA date (orig.) 8X 03 22 (rev.) 8X 07 14

7 294 Add laboratory accelerated corrosion testing

Conduct Design of Experiments (DOE) on wax thickness

1

Prepared by A. Tate—X6412—Body engr

Page

1

FMEA number

18 D Responsibility e R. Recommended and target t P. action(s) completion e N. date 19 20 c

6 Key date 9X 03 01 ER T. Fender—Car product dev., Childers—Manufacturing, J. Ford—Assy ops (Dalton, Fraser, Henley assembly plants) Core team

x

Potential Failure Mode and Effects Analysis (Design FMEA)



2

12

• Unsatisfactory appearance due to rust through paint over time • Impaired function of interior door hardware

Deteriorated life of door leading to:

11

10

Insufficient wax coverage over specified surface



Figure 20.16 Process FMEA example.

To cover inner door, lower surfaces at minimum wax thickness to retard corrosion

Manual application of wax inside door

Requirements

9

Process function

7

C S l e a v s s

5

2

8

Spray head deformed due to impact

Spray time insufficient

8

O c c u r

Spray heads clogged • Viscosity too high • Temperature too low • Pressure too low

Manually inserted spray head not inserted far enough

14

cause(s)/ mechanism(s) of failure

13 Potential

16

Current process controls

17

SAMPLE

Operator instructions and lot sampling (10 doors / shift) to check for coverage of critical areas

Preventative maintenance programs to maintain head

Test spray pattern at start-up and after idle periods, and preventative maintenance program to clean heads

Visual check each hour1/shift for film thickness (depth meter) and coverage

15

6

3

of

1450

28 None

7 392 Install spray timer

2

3 105 Use Design of Experiments (DOE) on viscosity vs. temperature vs. pressure

Automate spraying

5 280 Add positive depth stop to sprayer

1

1

4

Maintenance 9X 09 15

Mfg Engrg 9X 10 01

Mfg Engrg 9X 12 15

MFG Engrg 9X 10 15

Automatic spray timer installed operator starts spray, timer controls shut-off control charts show process is in control Cpk=2.05

Temp and press limits were determined and limit controls have been installed control charts show process is in control Cpk=1.85

Rejected due to complexity of different doors on same line

Stop added, sprayer checked on line

7

7

7

1

1

2

7

3

5

49

21

70


Action results 22 Actions taken

21

8

7 FMEA date (orig.) 9X 05 17 (rev.) 9X 11 06

Prepared by J. Ford—X6521—Assy ops

Page

1

FMEA number

18 D Responsibility e R. Recommended and target t P. action(s) completion e N. date 19 20 c

9X 08 26 Job #1

Process responsibility Body engrg./assembly operations

5 Model year(s) vehicle(s) 199X/Lion 4dr/wagon Key date 9X 03 01 ER A. Tate—Body engrg., J. Smith—OC, R. James—Production, J. Jones—Maintenance Core team

Item Front door L.H./H8HX-000-A

Potential Failure Mode and Effects Analysis (Process FMEA)

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The key result of an FMECA is a criticality matrix that ranks potential failures with respect to severity. The matrix then identifies a prioritization scheme for corrective actions based on the severity of potential failure modes. As displayed on an FMECA criticality matrix, potential failures plotted farther away from the matrix origin on a diagonal line represent higher potential risks of failure, and thus warrant increased need for corrective action.

EXAMPLE 20.7: FMECA EXAMPLE Figure 20.17 shows an example of an FMECA for a traveling lawn sprinkler. Note that each hardware item is listed on a separate line. For each possible failure, its effect on the product is determined. Type of failure is also shown along with estimates for its probability of occurrence and for its seriousness (Gryna, Chua, and Defeo 2007). For additional discussion of FMECA, consult Mil-Std 1629A for guidance in completing a criticality assessment. Designing for Quality

Component part number Worm bearing 4224

Possible failure Bearing worn

Zytel 101

Bearing stem 4225

Excessive wear

Brass

Thrust washer 4226

Excessive wear

Fulton 404

Worm 4527

Brass

Excessive wear in bearing area

T = Type of failure P = Probability of occurrence S = Seriousness of failure of system H = Hydraulic failure M = Mechanical failure W = Wear failure C = Customer abuse Cause of failure

Product

HRC-1

Date

Jan. 14, 2007

By

S.M.

T

P

S

Effect of failure on product

Not aligned with bottom housing

M

1

4

Spray head wobbles or slows down

Improve inspection

Excessive spray head wobble

M

1

3

Spray head wobbles or slows down

Improve worm bearing

Poor bearing/ material combination

M

5

4

Spray head wobbles and loses power

Change stem material

Dirty water in bearing area

M

5

4


Improve worm seal area


M

2

3


Improve operating instructions

High water pressure

M

2

5

Spray head will stall out

Inform customer in instructions

Dirty water in washers

M

5

5

Spray head will stall out

Improve worm seal design

Poor bearing/ material combination

M

5

4


Change bearing stem material

Dirty water in bearing area

M

5

4


Improve worm seal design


M

2

3


Improve operating instructions

Alternatives

Part III.E.4

1 = Very low (<1 in 1000) 2 = Low (3 in 1000) 3 = Medium (5 in 1000) 4 = High (7 in 1000) 5 = Very high (>9 in 1000)

Figure 20.17 Failure mode effects and criticality analysis.



Fault Tree Analysis Fault tree analysis (FTA) is a technique for analyzing complex systems to determine potential failure modes and probabilities of their occurrences. The technique was originated by H. A. Watson of Bell Telephone Laboratories to analyze the Minuteman Launch Control System. The following steps are required in order to develop fault trees (Dhillon and Singh 1981): 1. Define the undesired event (top event) of the system under consideration 2. Thoroughly understand the system and its intended use 3. Obtain the predefined system fault condition causes and continue the fault analysis to determine the relationships that can cause them

Part III.E.4

4. Construct a fault tree of logical relationships among input fault events To obtain quantitative results for the top event, assign failure probability, unavailability, failure, and repair rates data to basic events, provided the fault tree events are redundancy free. Fault tree analysis requires the construction of a fault tree diagram that represents the system conditions symbolically. This requires definition of the fault tree symbols. Such symbols include, for example, AND gate, OR gate, basic fault event, and priority AND gate. AND and OR gates are summarized as follows: AND gate—The AND gate denotes that the output event occurs if and only if all the input events occur. Its symbol is:

AND gate

Output

Inputs

OR gate—The OR gate denotes that the output event occurs if any of the input events occurs. Its symbol is:

OR gate

Output

Inputs

Exhaustive listings of fault tree symbols exist in specialized references (see Dhillon and Singh [1981] or Barlow, Fussell, and Singpurwalla [1975]). We demonstrate the use of FTA in the following example.



EXAMPLE 20.8 Construct a fault tree of a simple electric lamp. The top event is “no light” when the switch is turned on. This could be caused by: 1. Power failure E1 2. Switch fails to close E 2 3. Lamp failure E 3 4. Fuse failure E4 Furthermore, the power failure can be attributed to two events: major power failure or a fuse failure. A simple tree of these events is shown in Figure 20.18.

No light

No power

Intermediate event

Lamp failure

Part III.E.4

Switch fails

E3

E2

Power outage E1

Fuse failure E4

Figure 20.18 Simple fault tree.

Fault tree evaluations can be performed qualitatively or quantitatively. The qualitative evaluation determines the minimum cut sets (the minimum number of components that cause system failure) while the quantitative evaluation can be done using failure data for each component or event. The latter can be obtained from historical data or by using computer simulation. The result of the qualitative evaluation is a set of critical components and the result of the quantitative evaluation is the probability of the occurrence of the top event.



SUMMARY OF PART III

Part III.E.4

In Part III, basic definitions of reliability for both repairable and nonrepairable systems have been presented. In addition the basic relationships between the failure rate (hazard rate), probability density function, and reliability function have been developed. An example was provided to demonstrate how these functions can be obtained from failure data. Reliability estimations of simple systems made of series, parallel, or k-out-of-n components were obtained using the reliability of individual components. Maintainability of the systems was defined and three widely used maintenance and repair policies—corrective maintenance, preventive maintenance, and predictive maintenance—were discussed. Conditions for the applicability of these policies were also discussed. Finally, methods for identifying potential failures and their modes, such as FMEA, FMECA, ESS, and FTA, were discussed. FMEA is a tool to help crossfunctional teams identify, eliminate, and/or reduce the negative effects of potential failures—before they happen. FMEA is widely used as a stand-alone tool or as part of comprehensive quality systems/programs.



Part IV Product and Process Control Chapter 21 Chapter 22 Chapter 23 Chapter 24 Chapter 25 Chapter 26

A. B. C. D. E. F.

Tools Material Control Acceptance Sampling Measurement and Test Metrology Measurement System Analysis

Part III presented the different elements of product and process design that include: 1. Classification of quality characteristics 2. Design inputs and design review 3. Validation and qualification methods 4. Interpretation of technical drawings and specifications

Each of these five elements is critical to ensuring proper execution of the quality planning process. However, in a typical product development effort these elements will be executed in a different sequence. Design inputs (including quality and reliability goals, cost, size, timing targets, and so on) usually will occur first. As the design is developed, periodic reviews will be conducted to ensure that the design will meet the program goals. Element (4), interpretation of technical drawings and specifications, typically is treated within the context of the design reviews. Element (1) can be initiated once the design is sufficiently detailed. Validation and qualification methods may include computer simulation, such as finite element analysis, or traditional methods such as capability studies and reliability testing of prototype parts.

Part IV

5. Determining product and process control methods

179



Chapter 21 A. Tools

Define, identify, and apply product and process control methods such as developing control plans, identifying critical control points, developing and validating work instructions, etc. (Analyze). Body of Knowledge IV.A

Part IV.A

In this section, product and process control methods are presented. Such methods depend on the classification of quality characteristics and the results of validation tests. See Part III for more details. Characteristics that are critical to the operation of the process or the function of the product will be subject to more intense monitoring and control.

CONTROL PLANS Control plans are used to document and communicate the plan for monitoring and controlling the process. The control plan summarizes information from various sources into a single, handy document for quick reference on the production line. The format of the control plan is not important; standard spreadsheets are acceptable. However, the control plan should include the following elements: • Station or operation number and process description • Machinery, equipment, or fixtures • Reference drawing numbers • Product or process characteristic to be controlled (including tolerances) • Evaluation method (gages, sensors, visual checks, and so on) • Sample size and sample frequency – • Control method (X and R chart, check sheet, go/no-go, poka-yoke, and so on) 180


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 21: A. Tools 181

• Reaction plan to be followed when the control method detects a problem

• Containment • Diagnosis

Part IV.A

The control plan is the final link in a seamless chain that begins with the design FMEA. Potential failure modes that cannot be prevented through design are carried over to the process FMEA. Some failure modes can be prevented in the process through the use of poka-yokes or reduced to very low frequency through the use of designed experiments to optimize the process. Other failure modes can be detected with high confidence. Despite our best efforts, some potential failures may still have unacceptable RPNs, and process controls must be added to monitor the process. The control plan should be checked to verify that all critical and significant characteristics identified during the design and process FMEAs are included. At this point in the process, all nondestructive measurement systems listed on the control plan should have successfully passed the gage repeatability and reproducibility (R&R) requirement. (Gage R&R studies are discussed in Chapter 26.) Sample sizes and sample frequencies should be based on statistically sound principles. Keep in mind that the sample frequency should be often enough to enable containment of suspect product prior to shipment to the customer. The quality engineer plays a critical role in selecting the control method that is best suited for the characteristic being monitored. Perhaps the most important aspect of the control plan methodology is the reaction plan. The reaction plan lists the steps to be taken by the operator when the control method indicates a problem. For example, what should happen when the – X chart goes out of control? Unfortunately, many references and training seminars do not adequately develop this concept. The examples simply state, “adjust and recheck” or “recalibrate and recheck.” Simplistic directions may lead to process tampering (overadjustment). In addition, opportunities for permanent corrective actions will be missed. (See Chapter 37 for discussion of control charts.) Good reaction plans include four critical elements:

• Verification • Disposition Containment. As soon as the problem is identified, quarantine and segregate all suspect product. This may include everything produced since the previous acceptable sample. A good inventory management system that uses the principle of first in–first out will simplify the task of containment should it ever be needed. Provide specific direction to the operator on how to accomplish containment. It also may be wise to intensify inspection until the problem is resolved. Diagnosis. Determine the root cause of the failure. It may be necessary to ask repeatedly, “Why?” For example, if the failure occurred because the operator was not adequately trained, then ask, “Why was the operator not properly trained?” Repeat this process until an appropriate root cause is identified that will lead to a permanent corrective action. Incorporate lessons learned from previous failures


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 182 Part IV: Product and Process Control

to facilitate the diagnostic process. Remember that in the heat of battle common sense is not very common. Therefore, it is helpful to provide written guidance to the operator as to likely causes of the failure. In other words, specify the diagnostic steps and tests the operator should conduct during preliminary efforts to identify the root cause. If the root cause still is not identified, specify who should be called in to help, such as the product or quality engineer. Verification. Do not assume that the corrective action resolved the problem. Prove it! Collect additional samples after the corrective change is implemented to verify that the problem is fixed. If possible, the reaction plan should specify how many additional samples are necessary before resuming normal operations. Disposition. The obvious but nonetheless mandatory final step of the reaction plan is to determine an appropriate disposition for the material that was contained in the first step of the reaction plan. Typical dispositions include scrap, rework, sort, use as-is, and return to vendor. Written instructions are recommended for performing sorts or rework.

Part IV.A

See Figures 21.1 and 21.2 for an example of a control plan that incorporates many of the suggestions outlined above. It was developed by a valve manufacturing company. The author uses code letters in the reaction plan section of the control plan. Detailed reaction plan instructions are provided on the second page. Once the initial version of the control plan is released to production, the operators should take ownership of the document and treat it as a living document, constantly reviewing and updating it with new information. There also should be a feedback mechanism in the process—as new or unexpected failure modes are discovered on the line, update the control plan and feed the information back to update the FMEAs. Keeping the documentation current will facilitate the AQP process during future programs.

WORK INSTRUCTIONS Work instructions provide detail for personnel who have direct responsibility for the operation of the process. The instructions must be documented and posted or readily accessible at the work site. Assembly instructions list each task to be performed in sequential order. Setup instructions list appropriate machine settings, such as feed rates, temperatures, and pressures. Setup instructions also should list any tasks or inspections that must be performed during production start-up to verify that the process is properly adjusted. Work instructions must be clear and understandable. Liberal use of sketches, charts, photographs, and other visual aids is strongly encouraged. The effort to eliminate opportunities for error in the process should include the work instructions. Therefore, organizations that produce a variety of similar products should consider creating unique instructions for each model, rather than using generic examples, look-up tables for bills of material, cross-referenced set-up instructions, and so forth.


Print no.

Methods

Torque driver

Torque driver

Torque driver

Test tank

All

Pressure gage torque

Body-cover screw torque

Solenoid assembly torque

Final air test

All

18

23

27

29

All

209647 209648

209647 209648

209647 209648

714647 714648

714647 714648

714648

0.60 (minus) gage pin S/N 15-50-2118 Torque gage S/N 15-50-2019 Torque gage S/N 15-50-2120 Torque gage S/N 15-50-2019 Visual: ref. QA spec 203795 Functional: ref. assy instruction Visual

0.060” min diameter 20 +/– 5 IN LB

60 +/– 15 IN LB

14 +/– 7 IN LB

Functional test and leak check Workmanship

Evaluation measurement equipment

1

1

5

3 per screw

5

1

Size

100%

100%

1 per shift

2 per shift

1 per shift

1 per hour

Freq.

Sample

Go/no-go

Go/no-go

– X chart

Separate – X charts

– X chart

Check sheet

Control method

See note 2

A, B, C, D

E, F

E, F

E, F

A

Reaction plan code

Figure 21.1

Control plan example: page 1.

Part IV.A

Note 2: Compare suspect unit to visual accept/reject standards. If unit is unacceptable, stop the line and follow standard four-step reaction plan: (A) contain suspect units; (B) diagnose the root cause and implement corrective action; (C) verify that the corrective action is effective; (D) disposition suspect material (sort, scrap, rework, use as-is).

Note 1: At all times, quarantine one hour worth of product before releasing to shipping. In the event of a final test failure, the last hour of production should be set aside for possible retest. This should be done on all final test failures with the exception of porosity.

Drill press

Machine needle bleed port on cover

Process description

14

Sta #

Characteristic specification

Originator: J. Hausner

Product line: Soft start air dump valve

Part/assembly number/rev: 714647-H & 714648-J

Machine tools/ equipment

Revision date: 12/01/99

Control plan revision level: C

Control plan number: CP714

Soft Start-Up Valve Control Plan

SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 21: A. Tools 183


Containment: Segregate nonconforming product for MRB. Diagnosis: Verify torque. For torque adjustments, see Reaction Code “E” below. Ensure that diaphragm is installed correctly and that there are no voids present on the bead. Verify that the bead grooves on the bonnet and body are free of nicks or porosity and the diameters are within tolerance. Verify that the milled slot on the body is within tolerance. Contact PE if further diagnosis is required. Verification: Verify that corrective action eliminates problem. Disposition: Scrap nonconforming components. Rework assembly and retest. Contact line lead or PE if there are two or more consecutive failures or three failures within one hour.

Containment: Segregate nonconforming product for MRB. Diagnosis: Verify that fittings are installed correctly and have the correct torque applied. Verify that the threads on the fitting and assembly are free of nicks or porosity. Contact PE if further diagnosis is required. Verification: Verify that corrective action eliminates problem. Notify PE if another failure is found on the next 50 units. Disposition: Scrap nonconforming components. Rework assembly and retest.

Containment: Segregate nonconforming product for MRB. Diagnosis: Verify torque using another torque gage. For torque adjustments, take at least 10 samples and adjust torque gun if average is more than one standard deviation away from the nominal. Notify maintenance if average is close to nominal and there are any observations out of spec. Contact PE for further diagnosis. Verification: Measure a minimum of three subgroups and verify that the process is near nominal and in control. Disposition: If undertorqued, retorque assembly. If overtorqued, replace screw(s) and retorque.

Refer to QA/SPC procedure 231573. Comply with SPC procedure requirements. Document the root cause and corrective action in a note on the control chart.

Body–bonnet leak

Leak at fittings

Torque out of spec

SPC out of control, but parts in spec

Control plan example: page 2.

Containment: Segregate nonconforming product for MRB. Diagnosis: Verify that wire leads and power supply are hooked up correctly. Verify that flow control is open. Verify that diaphragm is installed correctly and check for voids in the seal bead. Verify that the dump hole is drilled completely through bonnet. Check that the fluid resistor is in place. Try another solenoid. If solenoid sticks open, quarantine current batch and switch to a new batch of solenoids. Contact PE if further diagnosis is required to determine cause. Verification: Verify that corrective action eliminates problem. Notify PE if another failure is found on the next 50 units. Disposition: Scrap nonconforming components. Rework assembly and retest.

Valve fails to close

Figure 21.2

Reaction plan

Containment: Segregate nonconforming unit and previous hour of production for MRB. Disposition: Verify that wire leads and power supply are hooked up correctly. Verify needle port diameter > 0.060”. If port diameter is under spec, switch to 100% inspection for the next 50 units and notify the product engineer (PE) if another failure is found. Replace drill bit if hole is not drilled through or burrs are present. Verify that piston ring is installed and free of nicks. Verify that needle valve is open at least one complete turn. Verify that the solenoid port resistor is installed. Try another solenoid. If other tests fail, check diameter of diaphragm. Contact the PE if additional diagnosis is required. Verification: Verify that corrective action eliminates problem. Disposition: Scrap nonconforming components. Rework assemblies as necessary and retest 100% of the previous hour of production.

Originator: J. Hausner

Valve fails to open

Product line: Airlogic control valve series

Part name/description: Soft start air dump valve HG & HJ series

Part/assembly number/rev: 714647-H & 714648-J

Revision date: 12/01/99

Failure mode

Control plan revision level: C

Key contact: J. Hausner

Control plan number: CP714

Soft Start-Up Valve Control Plan

Part IV.A

Code




Chapter 22 B. Material Control

M

aterial control addresses the raw materials, work in process, and final products and how they are physically controlled, identified, and tracked. The first step in control is classification; the last step is disposition. Material control is based on identification and classification. Systems, components, nonconformities, and features are all subject to classification schemes, and only after a classification has been done can the appropriate control be applied. There are many different classification factors that should be considered, including: • Volume of production • Complexity • Cost • Expected lifetime • Amount of maintenance required

If the product tends to be complex, expensive, and long-lived, then a great deal of effort must be expended in developing the material control scheme. Commoditytype products may require very little in the way of material control, but even the simplest products must be controlled—in simple and inexpensive ways. Such issues as process selection, inspection method, amount of sampling, strictness of inspection, and control of deviating material must be decided with respect to the importance of each characteristic and each component. Every good quality engineer should spend some time thinking about issues of relative importance and criticality. Think about the Pareto chart (see Chapter 27 for discussion of Pareto charts). Collect data and opinions so that before production ever begins, a scheme of relative importance is clearly established. This process requires careful study by a number of different individuals. It is an exercise in clarification, in making distinctions, and clearing up confusion. The task requires input from several different sources of expertise to assure that a balanced result is obtained. The people involved should include representatives of product design, safety, marketing, and field service.

Part IV.B

• Risk to safety and/or the environment

185



1. MATERIAL IDENTIFICATION, STATUS, AND TRACEABILITY

Define and distinguish these concepts, and describe methods for applying them in various situations. [Note: Product control procedures will not be tested.] (Analyze) Body of Knowledge IV.B.1

Identification of Materials The only reason to identify anything is to be able to trace it. And the only reason to trace it is to be able to find out something about it later. But those are two huge reasons in today’s highly technological and litigious society. Without product traceability, many manufacturers would be exposed to unacceptable risk.

Part IV.B.1

Principles of Identification Modern technology has produced a wide array of identification methods. The physical application of markings and subsequent tracking by means of scanners and sensors provide many options. It is necessary to maintain records not only of items produced and their identification but also of how the record-keeping system itself is operated and modified. After all, the storage and retrieval of information is a rapidly changing field. One of the most effective identification methods is radio frequency identification (RFID). By using radio frequency tags, information can be provided about, for example, identification, tracking, and security. RFID technology has been used in supply chain management, inventory tracking, and the healthcare industry. In healthcare, RFID technology has been implemented for asset management (determining where mobile medical devices are at all times, for example), patient care (determining where a patient is at all times while hospitalized), and inventory management (reducing the chance of inventory being out of stock at critical times).

Mechanics To illustrate the mechanics of product identification, consider the case of the Sauer Danfoss Company in Ames, Iowa. This company makes moderately complex mechanical products that require 100 percent testing and periodic design modifications. They have recently improved their materials management system by creating a multifunctional task team of four people. The team collected data for two and one-half years and finally decided to scrap their existing system for tracking material, which was dependent on manual entry into paper “move tags” and then manual keying into a computer database. Determination of current status required frequent physical count of all items.


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The team started all over with bar code and RFID (radio frequency identification) technologies. Now whenever an item of hardware moves, it is automatically accounted for, either by a bar code scanner or an RFID receiver. A sophisticated database system automatically processes each scan. The database maintains a variety of characteristics about each unit, including: • Model number • Unit number • Date produced • Result of test • Date of test • Rework record

• The amount of liability exposure

Part IV.B.1

Product identification is vital when producing complex products and often unnecessary for mundane commodities. However, a recent example of the lack of sufficient product identification and control was the Starlink seed corn problem of 2000. Starlink was a form of seed corn that was approved for growing animal feed but not for human consumption. There were inadequate controls put into place when the seed corn was sold to farmers and as a result the animal feed corn was inextricably intermixed with human-consumption corn at grain elevators throughout the Midwest. At the time they were delivering the corn, neither the farmers nor the grain elevator operators realized there was a problem. But soon, consumer groups were testing products made out of this corn, and the use of the unacceptable corn was detected. A great outcry resulted, and many losses were incurred as both types of the intermixed corn had to be converted to animal feed. Lennox Industries, in Marshalltown, Iowa, uses a 10-digit alphanumeric product identification code. This set of ten digits allows traceability to a diverse set of factors, including the date of fabrication, the supplier of each subsystem, the product model, and the date of final assembly. Several things must be considered when setting up such a code:

• The number of levels of components and subcomponents • The process design must incorporate the ability to trace products back to their point of creation and installation

Traceability Traceability is an explicit part of the ISO 9000 and ISO/TS 16949 standards. See paragraph 7.5.3 in ANSI/ISO/ASQ Q9001-2001, for example. Traceability is like a pedigree for a dog breed or the provenance of a painting. It allows one to find out about the past history of any item. Commodity products such as nuts and bolts have limited needs for traceability. Complex products such as automobiles must have multiple paths to trace back through many levels and many different sources. Sensitive material such as pharmaceuticals and food products must be traceable at all times. Even in the case of nuts and bolts, however, wise



manufacturers will keep different lots segregated and identified as long as it is economically possible. The ISO 9001 standard requires product identification and traceability, where appropriate, for recall of nonconforming product, hazardous product, or product in conflict with laws, regulations, or statutes. Product identification must be provided when required by a customer. Properly identified items must have a unique number and are tracked by their location in the process. Differences between items and lots must be distinguishable. The place to start with traceability, that is, the ability to preserve the identity of the product and its origins, is when the process is first designed. Today, appropriate software and database designs are available. Training of workers may be required in order to create the proper climate and means to accomplish this. Gryna (1988) listed four reasons why traceability is needed: 1. To assure that only materials and components of adequate quality enter the final product, for example, sterility of drug materials, adequate metallurgical composition, and heat treatment of structural components. 2. To assure positive identification to avoid mix-up of products that otherwise look alike. 3. To permit recall of suspected product on a precise basis. Lacking traceability programs, huge recalls of automobiles and other products have been required in the past. The number of defectives in the recalled set was often quite small.

Part IV.B.1

4. To localize causes of failure and take remedial action at minimal cost. There are other uses of traceability—such as in inventory control and scheduling. Some of these uses also affect quality. For example, use of materials on a first-in, first-out basis reduces the risk of quality deterioration of perishable materials. Factors to Consider • What is the cost of the product? A more expensive product requires more accountability over time, and thus better traceability. • How long will the product last? If it is going to be around a long time, there is more concern about its origin, as new discoveries often are made of chemical characteristics and environmental effects. The discovery that asbestos was a carcinogen after its routine use for decades is a good example. • Will the product be built into another product? • Does the product have items or materials in it that have not been thoroughly evaluated over a long period of time? • Is there a significant possible health hazard associated with the product? • Are field modifications often required, with different replacement items required on different models? (Automobiles are a prime example.)



Ten items to consider in a traceability program: 1. Product category 2. Product life 3. Unit cost 4. Recall or modification in the field 5. Product complexity 6. Level of downstream traceability 7. Documents providing traceability 8. Type of identification 9. Coded versus uncoded identification 10. Method of identification—tags, name plates, ink stamps, other means The use of a tracing code is required for efficient operation (Feigenbaum 1991). This code is established at the beginning of material flow and a traceability flowchart is established. The major activities on the flowchart include: 1. Critical component selection and listing by part number. 2. Vendor part coding (recording vendor name and date of receipt).

4. Computerized shipping records, including date codes, customer name, and destination. Correlation of these data with tracing code numbers results in very effective traceability of critical components.

2. MATERIAL SEGREGATION

Part IV.B.2

3. Coding internally manufactured parts, subassembly, assembly, and storage in a daily tally. At the end of the assembly line, each shipping container is date coded. This sequential coding procedure provides sufficient data to tie critical components to specific dates of receiving inspection, manufacturing, and final assembly.

Describe material segregation and its importance, and evaluate appropriate methods for applying it in various situations. (Evaluate) Body of Knowledge IV.B.2

There are two major situations that demand disposition of nonconforming products. The first is when a product fails to pass inspection or test and a decision regarding it must be made. This is the function of the material review board (MRB), to be discussed in section 4 of this chapter. The second situation, considerably



more serious, is when a problem develops after the product is out of the plant, on store shelves, in dealer showrooms, and in use by customers. Now a product recall may be required. In view of the very negative aspects of product recall, all the prior work concerning product traceability and product integrity will pay off quite handsomely in organizing the recall.

3. CLASSIFICATION OF DEFECTS

Define, describe, and classify the seriousness of product and process defects. (Evaluate)

Part IV.B.3

Body of Knowledge IV.B.3

In certain types of products, more than one defect could be present and a relatively small number of minor defects could be acceptable to the customer. Product quality in these cases may be judged by the total number of defects or the number of defects per unit. Control charts for attributes are a tool that may be used for this purpose. In such cases, the objective of inspection is to determine the number of defects or nonconformities present rather than to classify units as conforming or nonconforming. Defect and nonconformity are two terms that may be used synonymously in many situations. For other purposes, the definitions of both terms are slightly different. A nonconformity is defined as a failure of a quality characteristic to meet its intended level or state, occurring with severity sufficient to cause the product not to meet a specification. A defect is a nonconformity severe enough to cause the product not to satisfy normal usage requirements. Thus, the difference between the term nonconformity and the term defect is based mainly on perspective. The former is defined based on specifications, while the latter is defined based on fitness for use. The numerical result generated by inspection consists of the count of defects or nonconformities for each product unit. Often it is possible to classify the different types of defects according to their severity, and then assign a weight to each class based on the importance of the affected quality characteristic that relates to the product specifications. The selection of the weights should reflect the relative importance of the various defect categories and their likelihood of causing product failure or customer dissatisfaction. A typical seriousness classification includes four levels of defect seriousness: 1. Critical defect may lead directly to severe injury or catastrophic economic loss. 2. Serious defect may lead to injury or significant economic loss. 3. Major defect may cause major problems during normal use. A major defect will likely result in reducing the usability of the product.



4. Minor defect may cause minor problems during normal use. See Montgomery (2009b) for discussion of defect levels.

4. MATERIAL REVIEW BOARD (MRB)

Identify the purpose and function of an MRB, and make appropriate disposition decisions in various situations. (Analyze) Body of Knowledge IV.B.4

The material review board (MRB) is an appointed group of individuals with different backgrounds and expertise. Their assignment is to determine what corrective actions must be taken after nonconforming parts or components are discovered. In a larger sense, the purposes of the MRB are to determine the disposition of nonconforming parts, components, and subassemblies, determine the causes of the nonconformance of these items, and take the necessary corrective actions to prevent such nonconformance from taking place in future production. The basic function of a material review board is to: (a) review material that does not conform to standard, (b) determine what its disposition should be, and (c) drive the development of effective corrective action to prevent recurrence. The MRB is a broad-based reviewing agency whose membership usually consists minimally of representatives from the following:

• Quality assurance. The representative is often from quality control engineering • Customers. The representative may be from the customer’s organization (for example, the government inspector) or from marketing

Part IV.B.4

• Engineering. The cognizant designer is often the representative

In some companies, the role of the material review board is solely one of judging fitness for use of nonconforming products. Bond (1983) discusses board composition, philosophy, and problem documentation. In general, the MRB procedural steps can be summarized as follows: After a defect is discovered, verification by inspection may be needed. A complete description of any nonconformance is then initiated. A quality engineer picked by the MRB will review the facts and include the case in an appropriate tracking system. The MRB committee may then follow up with investigation and analysis. When done, the quality engineer takes the case again, recommending the appropriate corrective action(s) and steps for implementation. The term standard repair is common within the MRB framework. It signifies a procedure where a certain type of defect(s) occurs time and time again. A standard repair procedure is then initiated, documented, and implemented for such



Part IV.B.4

Table 22.1

Standards pertaining to material review board operations.

Standard

Purpose

MIL-STD-1520C

Sets “the requirements for cost-effective corrective action and disposition system for nonconforming material”

MIL-Q-9858A

Quality program requirements, Section 6.5, Nonconforming Material, requires the contractor to establish “an effective and positive system for controlling nonconforming material”

MIL-STD-481B

Configuration control—Engineering changes

MIL-I-8500

Establishes interchangeability and replaceability requirements

ANSI/ASQ Z1.4-2003

Sampling procedures and tables for inspection by attributes

ANSI/ASQ Z1.9-2003

Sampling procedures and tables for inspection by variables for percent defectives

situations. Minor defects are most likely to be treated with a standard repair procedure. Within the context of defect classification, defects may further be classified as major or minor. Minor defects, unlike major ones, may not adversely affect the integrity of the part, component, or assembly. In many cases, the MRB concludes that the lot containing nonconforming products should not be shipped as is. The decision, concurred with by inspection personnel, may be: sort (100 percent inspection), downgrade, repair, rework, scrap, and so on. A decision to ship also may be authorized by the MRB. In such cases, a unanimous decision should be reached by all members. The decision also should create factual data and thus an important source of information. A successful MRB program requires that the board not only make decisions about immediate disposition of rejected material, but also direct ongoing programs of root cause analysis to eliminate future rejections of the same type. There are several military documents associated with the MRB concept. A partial list is shown in Table 22.1.



Chapter 23 C. Acceptance Sampling

A

cceptance sampling is a method for inspecting the product. Inspection can be done with screening (also called sorting or 100 percent inspection), in which all units are inspected, or with sampling. Acceptance sampling is the process of inspecting a portion of the product in a lot for the purpose of making a decision regarding classification of the entire lot as either conforming or nonconforming to quality specifications. Whether inspection is done with screening or with sampling, the results of inspection can be used for different purposes as follows: 1. To distinguish between good lots and bad lots using acceptance sampling plans (as in incoming material inspection and final product inspection). 2. To distinguish between good products and bad products. 3. To determine the status of process control and if the process is changing. This is usually done in conjunction with control charts.

5. To determine process adjustment. Based on inspection results of process output, as depicted by a histogram for example, the process mean may require adjustment and/or process variation may need to be reduced. A process might require adjustment even though all the units produced to date conform to the quality standards agreed upon with the customer.

Part IV.C

4. To evaluate process capability. In this case, inspection is used to determine if the process exhibits excessive variation and if it is approaching or exceeding the specification limits.

6. To rate the accuracy of inspectors or of inspection equipment by comparing the inspection results with corresponding standards. An inspection operation can result in two types of error: classification of a conforming unit as nonconforming and classification of a nonconforming unit as conforming. The probabilities of both types of error can be easily estimated using probability theory and other statistical methods.

193



7. To serve as a mechanism for evaluating vendors in terms of their products’ quality. Vendors that consistently deliver high-quality products can receive preferred status involving reduced inspection and priority in bidding for new contracts, while vendors that do not stand up to quality requirements could be warned or discontinued altogether. This type of procedure is known as vendor qualification or vendor certification. The last three uses of inspection might be seen as feedback about the production processes, the measurement processes, and the supplier.

Part IV.C

SAMPLING INSPECTION VERSUS 100 PERCENT INSPECTION Sampling provides the economic advantage of lower inspection costs due to fewer units being inspected. In addition, the time required to inspect a sample is substantially less than that required for the entire lot and there is less damage to the product due to reduced handling. Most inspectors find that selection and inspection of a random sample is less tedious and monotonous than inspection of the complete lot. Another advantage of sampling inspection is related to the supplier/ customer relationship. By inspecting a small fraction of the lot and forcing the supplier to screen 100 percent in case of lot rejection (which is the case for rectifying inspection), the customer emphasizes that the supplier must be concerned about quality. On the other hand, the variability inherent in sampling results in sampling errors: rejection of lots of conforming quality and acceptance of lots of nonconforming quality. Acceptance sampling is most appropriate when inspection costs are high and when 100 percent inspection is monotonous and can cause inspector fatigue and boredom, resulting in degraded performance and increased error rates. Obviously, sampling is the only choice available for destructive inspection. Rectifying sampling is a form of acceptance sampling. Sample units detected as nonconforming are discarded from the lot, replaced with conforming units, or repaired. Rejected lots are subject to 100 percent screening, which can involve discarding, replacing, or repairing units detected as nonconforming. In certain situations, it is preferable to inspect 100 percent of the product. This would be the case for critical or complex products, where the cost of making the wrong decision would be too high. Screening is appropriate when the fraction nonconforming is extremely high. In this case, most of the lots would be rejected under acceptance sampling and those accepted would be so as a result of statistical variations rather than better quality. Screening is also appropriate when the fraction nonconforming is not known and an estimate based on a large sample is needed.


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1. SAMPLING CONCEPTS

Define, describe, and apply the concepts of producer and consumer risk and related terms, including operating characteristic (OC) curves, acceptable quality limit (AQL), lot tolerance percent defective (LTPD), average outgoing quality (AOQ), average outgoing quality limit (AOQL), etc. (Analyze) Body of Knowledge IV.C.1

Sampling may be performed according to the type of quality characteristics to be inspected. There are three major categories of sampling plans: sampling plans for attributes, sampling plans for variables, and special sampling plans. It should be noted that acceptance sampling is not advised for processes in continuous production and in a state of statistical control. For these processes, Deming (1986) provides decision rules for selecting either 100 percent inspection or no inspection.

Lot-by-Lot versus Average Quality Protection

Part IV.C.1

For continuing processes, sampling plans based on average quality protection have characteristics calculated from the binomial and/or Poisson distributions. For processes not considered to be continuing, sampling plans based on lot-by-lot protection have characteristics calculated from the hypergeometric distribution, which takes the lot size into consideration. Sampling plans based on the Poisson and binomial distributions are more common than those based on the hypergeometric distribution. This is due to the complexity of calculating plans based on the hypergeometric distribution. New software on personal computers, however, may eliminate this drawback.

The Operating Characteristic Curve No matter which type of attribute sampling plan is being considered, an important evaluation tool is the operating characteristic (OC) curve. The OC curve allows a sampling plan to be almost completely evaluated at a glance, giving a pictorial view of the probabilities of accepting lots submitted at varying levels of percent nonconforming. The OC curve illustrates the risks involved in acceptance sampling. Figure 23.1 shows an OC curve for a sample size n of 50 drawn from an infinite lot size, with an acceptance number c of 3. As can be seen by the OC curve, if the lot were 100 percent to specifications, the probability of acceptance Pa also would be 100 percent. But if the lot were 13.4 percent defective, there would be approximately a 10 percent probability of acceptance.



Probability of acceptance Pa

1.0

0.8

0.6

0.4

0.2

0.0 0.00

Part IV.C.1

Figure 23.1

0.05 0.10 0.15 Fraction nonconforming p

0.20

An operating characteristic (OC) curve for n = 50 and c = 3.

There are two types of OC curves to consider: (1) type A OC curves and (2) type B OC curves. Type A OC curves are used to calculate the probability of acceptance on a lot-by-lot basis when the lot is not a product of a continuous process. These OC curves are calculated using the hypergeometric distribution. Type B OC curves are used to evaluate sampling plans for a continuous process or for a process where a lot of size N is large. These curves are based on the binomial and/or Poisson distributions when the requirements for usage are met. In general, the ANSI/ASQ Z1.4-2003 standard OC curves are based on the binomial distribution for sample sizes through 80 and the Poisson approximation to the binomial for sample sizes greater than 80. The Poisson approximation to the binomial was often employed for calculating the probability of acceptance (Pa) when the sample sizes of interest were quite large. This approximation was used because the computations needed to calculate the binomial probabilities could be impractical. With modern spreadsheet software, the binomial computations are no longer a problem. However, you still see Poisson approximations used for the binomial distribution, and this is acceptable.

Plotting the OC Curve In the examples that follow, it is assumed that the process of interest is continuous (in theory). Since the process is considered continuous, the lot size is not taken into consideration in the calculations of Pa. Suppose a sample size of n = 50 is randomly selected from the lot. Furthermore, the lot is accepted if three or fewer nonconformances are found in the sample. To plot the OC curve, six to eight representative points for fraction nonconforming should be used to draw the continuous curve through the points.



EXAMPLE 23.1 Consider a sampling plan with n = 50 and an acceptable number of nonconforming units of at most three (c = 3). The probability of acceptance of the lot can be found for various values of p using equation (23–1). The probabilities are given in Table 23.1. The resulting OC curve is displayed in Figure 23.1 on the previous page. Table 23.1

Probability of acceptance for various levels of fraction nonconforming. p

Pa

0.01

0.9984

0.02

0.9822

0.03

0.9372

0.04

0.8609

0.05

0.7604

0.06

0.6473

0.07

0.5327

0.08

0.4253

0.09

0.3303

0.10

0.2503

0.15

0.0460

0.20

0.0057

c

Pa = P ( d ≤ c ) = ∑ n Cd p d ( 1 − p )

n− d

Part IV.C.1

The probability of acceptance Pa can be calculated using the binomial probability mass function (see Chapter 34 for complete details on the binomial distribution and the cumulative distribution function). The probability of acceptance is given by (23–1)

d= 0

where d = the number of nonconforming items c = the acceptance number p = fraction nonconforming n

Cd =

n! and represents the number of ways that d d ! ( n − d )! nonconforming units can be chosen from a sample of size n.



The OC curve can be constructed similarly to the one in Figure 23.1 for various values of p. The probabilities of acceptance can be calculated using the Poisson distribution (discussed in Chapter 34). The probability of acceptance is c

Pa = P ( d ≤ c ) = ∑ d= 0

( np )

d

e − np

d!

(23–2)

where np is the mean of the binomial distribution and therefore the necessary parameter for the Poisson distribution. Complete details on the Poisson and binomial distributions can be found in Chapter 34. Furthermore, tables providing these probabilities can also be found in Appendix J for the binomial distribution and in Appendix M for the Poisson distribution. The operating characteristic curve is useful for a number of quantities of interest. Two of those quantities are the acceptable quality limit (AQL) and the lot tolerance percent defective (LTPD).

Part IV.C.1

Acceptance Sampling by Attributes Acceptance sampling by attributes generally is used for two purposes: (1) protection against accepting lots from a continuing process whose average quality deteriorates beyond an acceptable quality level, and (2) protection against isolated lots that may have levels of nonconformances greater than can be considered acceptable. The most commonly used form of acceptance sampling plan is sampling by attributes. The most widely used standard of all attribute plans, although not necessarily the best, is ANSI/ASQ Z1.4-2003. The following sections provide more details on the characteristics of acceptance sampling and discussion of military standards in acceptance sampling.

Acceptable Quality Limit As part of the revision of ANSI/ASQC Z1.4-1993, acceptable quality level (AQL) has been changed to acceptable quality limit (AQL) in ANSI/ASQ Z1.4-2003 and is defined as the quality level that is the worst tolerable process average when a continuing series of lots is submitted for acceptance sampling. This means that a lot that has a fraction defective equal to the AQL has a high probability (generally in the area of 0.95, although it may vary) of being accepted. As a result, plans that are based on AQL, such as ANSI/ASQ Z1.4-2003, favor the producer in getting lots accepted that are in the general neighborhood of the AQL for fraction defective in a lot.

Lot Tolerance Percent Defective The lot tolerance percent defective (LTPD), expressed in percent defective, is the poorest quality in an individual lot that should be accepted. The LTPD has a low probability of acceptance. In many sampling plans, the LTPD is the percent defective having a 10 percent probability of acceptance.



Producer’s and Consumer’s Risks There are risks involved in using acceptance sampling plans. The risks involved in acceptance sampling are: (1) producer’s risk and (2) consumer’s risk. These risks correspond with type I and type II errors in hypothesis testing (type I and type II errors are discussed in Chapter 35). The definitions of producer’s and consumer’s risks are: Producer’s risk (a ). The producer’s risk for any given sampling plan is the probability of rejecting a lot that is within the acceptable quality level (see ASQ [2004]). This means that the producer faces the possibility (at level of significance a ) of having a lot rejected even though the lot has met the requirements stipulated by the AQL level. Consumer’s risk (b ). The consumer’s risk for any given sampling plan is the probability of acceptance (often 10 percent) for a designated numerical value of relatively poor submitted quality (ASQ 2004). The consumer’s risk, therefore, is the probability of accepting a lot that has not met the requirements stipulated by the LTPD level.

Average Outgoing Quality

Part IV.C.1

The average outgoing quality (AOQ) is the expected average quality of outgoing products, including all accepted lots, plus all rejected lots that have been sorted 100 percent and have had all of the nonconforming units replaced by conforming units. There is a given AOQ for specific fractions nonconforming of submitted lots sampled under a given sampling plan. When the fraction nonconforming is very low, a large majority of the lots will be accepted as submitted. The few lots that are rejected will be sorted 100 percent and have all nonconforming units replaced with conforming units. Thus, the AOQ will always be less than the submitted quality. As the quality of submitted lots declines in relation to the AQL, the percent of lots rejected increases in proportion to accepted lots. As these rejected lots are sorted and combined with accepted lots, an AOQ lower than the average fraction of nonconformances of submitted lots emerges. Therefore, when the level of quality of incoming lots is good, the AOQ is good; when the incoming quality is bad and most lots are rejected and sorted, the result is also good. To calculate the AOQ for a specific fraction nonconforming and a sampling plan, the first step is to calculate the probability of accepting the lot at that level of fraction nonconforming. Then, multiply the probability of acceptance by the fraction nonconforming for the AOQ. Thus, n⎤ ⎡ AOQ = Pa p ⎢1 − ⎥ ⎣ N⎦ where N is the lot size and n is the sample size. If the desired result is a percentage, multiply by 100. If the lot size is assumed infinite (theoretically) then AOQ ≅ Pap. The average outgoing quality limit (AOQL) is the maximum AOQ for all possible levels of incoming quality.



Average Outgoing Quality Limit The AOQ is a variable dependent on the quality level of incoming lots. When the AOQ is plotted for all possible levels of incoming quality, a curve as shown in Figure 23.2 results. The average outgoing quality limit (AOQL) is the highest value on the AOQ curve. Assuming an infinite lot size, the AOQ may be calculated as AOQ = Pap. The probability of acceptance can be calculated using equation (23–1) or equation (23–2) as before. An average outgoing quality curve can be constructed for various fractions nonconforming (p) and probabilities of acceptance (Pa). The maximum AOQ is the average outgoing quality limit (AOQL).

Lot Size, Sample Size, and Acceptance Number For any single sampling plan, the plan is completely described by the lot size, sample size, and acceptance number. In this section, the effect of changing the sample size, acceptance number, and lot size on the behavior of the sampling plan will be explored along with the risks of constant percentage plans.

EXAMPLE 23.2

Part IV.C.1

Consider the previous example with n = 50, c = 3, and p = 0.01 to 0.10 by 0.01. The AOQ values are given in Table 23.2. The resulting AOQ curve is displayed in Figure 23.2. The AOQL is approximately 0.03884.

Table 23.2 AOQ levels for various levels of fraction nonconforming. p

Pa

AOQ

0.01

0.9984

0.00998

0.02

0.9822

0.01964

0.03

0.9372

0.02812

0.04

0.8609

0.03444

0.05

0.7604

0.03802

0.06

0.6473

0.03884

0.07

0.5327

0.03729

0.08

0.4253

0.03402

0.09

0.3303

0.02973

0.10

0.2503

0.02503

0.15

0.0460

0.00690

0.20

0.0057

0.00114



The effect on the OC curve caused by changing the sample size while holding all other parameters constant is shown in Figure 23.3. The probability of acceptance changes considerably as sample size changes. The Pa for the given sample sizes for 0.04

AOQ

0.03

0.02

0.01

0.00 0.00

Figure 23.2

0.05 0.10 0.15 p (fraction nonconforming)

0.20

AOQ curve for n = 50, c = 3, and infinite lot size.

1.00

n=2

0.80

n=4

0.70

n = 10

Pa

0.60

Part IV.C.1

0.90

n=1

0.50 0.40 0.30 0.20 0.10 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

1.0

p (fraction nonconforming)

Figure 23.3

Effect on an OC curve of changing sample size (n) when accept number (c) is held constant.



Table 23.3 Probability of acceptance for various n. Sample size (n)

Probability of acceptance (Pa%)

10

35

4

66

2

81

1

90

1.00 0.90 0.80 0.70

n = 10 c=0 c=1 c=2

Pa

0.60 Indifference quality level 0.50 0.40 0.30 0.20

Part IV.C.1

0.10 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

1.0

p (fraction nonconforming)

Figure 23.4

Effect of changing accept number (c) when sample size (n) is held constant.

a 10 percent nonconforming lot and an acceptance number of zero are shown in Table 23.3. The effect of changing the acceptance number on a sampling plan while holding all other parameters constant is shown in Figure 23.4. Another point of interest is that for c = 0, the OC curve is concave in shape, while plans with larger acceptance numbers have a “reverse s” shape. Figure 23.4 and Table 23.4 show the effect of changing the acceptance number of a sampling plan on the indifference quality level (IQL: 50–50 chance of accepting a given percent defective). The parameter having the least effect on the OC curve is the lot size N. Figure 23.5 shows the changes in the OC curve for a sample size of 10, acceptance number of 0, and lot sizes of 100, 200, and 1000. For this reason, using the binomial and



Table 23.4 Fraction defective at indifference quality level. Sample size (n)

Acceptance number (c)

Percent defective at indifference quality level

10

2

0.26

10

1

0.17

10

0

0.07

1.00 0.90 n = 10 0.80 c=0 0.70

Pa

0.60 0.50 0.40

N = 1000 N = 200 N = 100

0.30 0.20

0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.0 p (fraction nonconforming) Figure 23.5

Part IV.C.1

0.10

Effect of changing lot size (N) when acceptance number (c) and sample size (n) are held constant.

Poisson approximations, even when lot sizes are known (and are large compared to sample size), results in little error in accuracy. Some key probabilities of acceptance points for the three lot sizes are displayed in Table 23.5. As can be seen, the differences due to lot size are minimal. Computing the sample size as a percentage of the lot size has a large effect on risks and protection, as shown in Figure 23.6. In this case, plans having a sample size totaling 10 percent of the lot size are shown. As can be seen, the degree of protection changes dramatically with changes in lot size, which results in low protection for small lot sizes and gives excessively large sample requirements for large lot sizes.



Table 23.5

Probability of acceptance for various lot sizes.

Fraction defective (p)

Probability of acceptance (Pa )

Lot size (N)

0.10

0.330

100

0.30

0.023

100

0.50

0.001

100

0.10

0.340

200

0.30

0.026

200

0.50

0.001

200

0.10

0.347

1000

0.30

0.028

1000

0.50

0.001

1000

1.00 0.90

N = 250, n = 25, c = 0

0.80 N = 100, n = 10, c = 0

0.70

Pa

Part IV.C.1

0.60 N = 50, n = 5, c = 0

0.50 0.40 0.30 0.20 0.10 0 0.10

Figure 23.6

0.20 0.30 0.40 p (fraction nonconforming)

0.50

Operating characteristic curves for sampling plans having the sample size equal to 10 percent of the lot size.



2. SAMPLING STANDARDS AND PLANS

Interpret and apply ANSI/ASQ Z1.4 and Z1.9 standards for attributes and variables sampling. Identify and distinguish between single, double, multiple, sequential, and continuous sampling methods. Identify the characteristics of Dodge-Romig sampling tables and when they should be used. (Analyze) Body of Knowledge IV.C.2

There are several types of attribute sampling plans in use, with the most common being single, double, multiple, and sequential sampling plans. The type of sampling plan used is determined by ease of use and administration, general quality level of incoming lots, average sample number, and so on.

Single Sampling Plans

Double Sampling Plans When using double sampling plans, a smaller first sample is taken from the submitted lot, and one of three decisions is made: (1) accept the lot, (2) reject the lot, or (3) draw another sample. If a second sample is to be drawn, the lot will either be accepted or rejected after the second sample. Double sampling plans have the advantage of a lower total sample size when the incoming quality is either excellent or poor because the lot is either accepted or rejected on the first sample.

Part IV.C.2

When single sampling plans are used, the decision to either accept or reject the lot is based on the results of the inspection of a single sample of n items from a submitted lot. In Example 1, the OC curve and AOQ curve were calculated for a single sampling plan where n = 50 and c = 3. Single sampling plans have the advantage of ease of administration, but due to the unchanging sample size they do not take advantage of the potential cost savings of reduced or tightened inspection when incoming quality is either excellent or poor.

EXAMPLE 23.3 A double sampling plan is to be executed as follows: take a first sample (n 1) of 75 units and set c 1 (the acceptance number for the first sample) at 0. The lot will be accepted based on the first sample results if no nonconformances are found in the first sample. Continued



Continued

If three nonconformances are found in the first sample, the lot will be rejected based on the first sample results. If after analyzing the results of the first sample one or two nonconformances are found, take a second sample (n 2 = 75). The acceptance number for the second sample (c 2) is set to 3. If the combined number of nonconformances in the first and second samples is three or fewer, the lot will be accepted and if the combined number of nonconformances is four or more, the lot will be rejected. The plan is represented as follows: Sample size

Acceptance number (c)

Rejection number (r)

n 1 = 75

c1 = 0

r1 = 3

n 2 = 75

c2 = 3

r2 = 4

OC Curve for a Double Sampling Plan

Part IV.C.2

To calculate the OC curve for a double sampling plan, equation (23–1) and equation (23–2) can again be utilized. To calculate probabilities of acceptance, some arbitrary points for p are chosen to cover the range of the OC curve. The fraction defective p is then multiplied by n1 (the first sample) or n2 (the second sample) to determine the expected value np. The generalized formula for calculating the probability of acceptance (Pa) is: Pa = p0 + (p1p2 + p1p1 + p1p0) + (p2p1 + p2p0) where: p0 = probability of zero nonconformances in first sample p1p2 = probability of one nonconformance in first sample times the probability of two nonconformances in the second sample, and so on.

EXAMPLE 23.4 For a double sampling plan where n 1 = 75, c 1 = 0, r 1 = 3, n 2 = 75, c 2 = 3, r2 = 4, show the computations for the OC curve. To determine the technique of plotting the OC curve, three points for p may be used (0.01, 0.04, and 0.08), although in practice six to ten should be used. The points for the OC curve are calculated using the generalized equation for each fraction nonconforming, selected as follows: Continued



Continued

Generalized equation values

p = 0.01

p = 0.04

p0

0.4720

0.050

0.002

p = 0.08

p 1p 0

0.1676

0.0075

0.00003

p 1p 1

0.1260

0.0222

0.000225

p 1p 2

0.0471

0.0334

0.000675

p2p 0

0.0627

0.0112

0.00009

p2p1

0.0471

0.0334

0.000675

Totals for Pa

0.9226

0.1577

0.003695

These points are used to construct the OC curve for the double sampling plan as shown in Figure 23.7. 1.00 0.90 0.80 0.70

Pa

0.60

n1 = 75, c1 = 0, r1 = 3

0.50 n2 = 75, c2 = 3, r2 = 4 0.40

0.20 0.10 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 p (fraction nonconforming)

Figure 23.7

Part IV.C.2

0.30

OC curve for double sampling plan where n1 = 75, c1 = 0, r1 = 3, n2 = 75, c2 = 3, r2 = 4.

Multiple Sampling Plans Multiple sampling plans work in the same way as double sampling with an increase in the number of samples to be taken up to seven, according to ANSI/ASQ Z1.42003. In the same manner that double sampling is performed, acceptance or rejection of submitted lots may be reached before the seventh sample, depending on the acceptance/rejection criteria established for the plan.



AOQ (%) 3.0 n1 = 75, c1 = 0, r1 = 3 n2 = 75, c2 = 3, r2 = 4

2.0

1.0

0 1

2

3

4

5

6

7

8

9

10

p (percent nonconforming)

Figure 23.8

Average outgoing quality curve for double sampling plan.

AOQ and AOQL for Double and Multiple Plans

Part IV.C.2

The AOQ curve and AOQL for double and multiple sampling plans are plotted and determined in the same manner as for single sampling plans. An AOQ curve for a double sampling plan is shown in Figure 23.8; the AOQL is approximately 1.3 percent.

Average Sample Number The average sample number (ASN) is a determination of the expected average amount of inspection per lot for a given sampling plan. The ASN for single sampling plans is a constant value that is equal to the single sample size for the plan. The ASN for double sampling plans is the sum of first sample size plus the second sample size times the probability that a second sample will be required. The ASN is also a function of fraction noncomforming when working with a double sampling plan. The double sampling plan ASN formula is: ASN = n1 + n2(P2) where: n1 = size of first sample n2 = size of second sample P2 = probability of requiring a second sample



EXAMPLE 23.5 The double sampling plan in the earlier section was n 1 = 75 n 2 = 75

c1 = 0 c2 = 3

r1 = 3 r2 = 4

• A second sample is required if on the first sample one or two nonconformances are noted. • If zero nonconformances are found in the first sample, the lot is accepted. • If three or more nonconformances are found in the first sample, the lot is rejected. Denote the probability of making a decision, accept or reject, on the first sample as P(D 1). Then, P(D 1) = P(0) + P (3 or more) P(0) = the probability of zero nonconformances on the first sample P (3 or more) = the probability of three or more nonconformances on the first sample. P 2 = 1 – P(D 1), then, ASN = n 1 + n 2(P 2) When using the Poisson table to calculate the probability of three or more nonconformances, remember that the probability of three or more nonconformances is given by: (1 – probability of two or less nonconformances) in the sample

When p = 0.01: P(0) = Probability of zero nonconformances in sample = 0.4724 P (3 or more) = Probability of three or more nonconformances in sample = 0.0410

Part IV.C.2

The average sample number will be plotted for several values of fraction nonconforming p and an ASN curve will be plotted. An example of the ASN calculation for the fraction nonconforming p = 0.01 is shown below. Several other points need to be plotted for other values of p. Figure 23.9 shows an ASN curve for the example.

P(D 1) = Probability of a decision on the first sample (using the above equation) = 0.4724 + 0.0410 = 0.5134 Then P 2 = probability of requiring a second sample = 1 – 0.5134 = 0.4866. Thus the ASN is ASN(0.01) = Average sample number for a lot quality p = 0.01 = n 1 + n 2 (P 2) = 75 + 75 (0.4866) = 111.50 or 112 Continued



Continued ASN 130

n1 = 75, c1 = 0, R1 = 3

120

n2 = 75, c2 = 3, R2 = 4

110 100 90 80 70 60 50 40 30 20 10 0

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 Fraction nonconforming

Figure 23.9

Average sample number curve for double sampling plan.

Values of ASN at different p values (ASNp) may be calculated in a similar way; the results are given below. All values are rounded to the next highest integer.

Part IV.C.2

ASN(0.01) = 112 ASN(0.06) = 87 ASN(0.02) = 119 ASN(0.07) = 82 ASN(0.03) = 113 ASN(0.08) = 79 ASN(0.04) = 103 ASN(0.09) = 78 ASN(0.05) = 94 ASN( 0.1) = 77 When comparing sampling plans with equal protection, double sampling plans will generally result in smaller average sample sizes when quality is excellent or poor. When quality is near the indifference level, double sampling plans will rarely result in greater ASN.

ANSI/ASQ Z1.4-2003* ANSI/ASQ Z1.4-2003 is a revision of ANSI/ASQC Z1.4-1993 incorporating eight changes that include (ANSI/ASQ [2003]): 1. Acceptable quality level (AQL) has been changed to acceptable quality limit (AQL). * The tables for these and other standards that appear in Chapter 23 of the previous edition of this handbook are now available on the CD-ROM accompanying this edition.



2. The definition and explanation of AQL have been changed. See the new definition of AQL above. 3. The discontinuation of inspection rule has been changed. See later sections in this chapter. 4. ANSI/ASQC A2-1987 has been changed to ANSI/ASQ A3534-2-1993. 5. ANSI/ASQC Q3 has been changed to ASQC Q3-1988. Other than the above changes and some changes to the footnotes of some tables, all tables, table numbers, and procedures used in MIL-STD-105E (which was canceled in 1995) and ANSI/ASQC Z1.4-1993 have been retained. ANSI/ASQ Z1.4-2003 is probably the most commonly used standard for attribute sampling plans. The wide recognition and acceptance of the plan could be due to government contracts stipulating the standard rather than its statistical importance. Producers submitting products at a nonconformance level within AQL have a high probability of having the lot accepted by the customer. When using ANSI/ASQ Z1.4-2003, the characteristics under consideration should be classified. The general classifications are critical, major, and minor defects: • Critical defect. A defect that judgment and experience indicate is likely to result in hazardous or unsafe conditions for the individuals using, maintaining, or depending on the product, or a defect that judgment and experience indicate is likely to prevent performance of the unit. In practice, critical characteristics are commonly inspected to an AQL level of 0.40 to 0.65 percent if not 100 percent inspected. One hundred percent inspection is recommended for critical characteristics if possible. Acceptance numbers are always zero for critical defects.

• Minor defect. A defect that is not likely to reduce materially the usability of the unit of product for its intended purpose. In practice, AQL levels for minor defects generally range from 1.5 percent to 2.5 percent.

Part IV.C.2

• Major defect. A defect, other than critical, that is likely to result in failure or to reduce materially the usability of the unit of product for its intended purpose. In practice, AQL levels for major defects are generally about one percent.

Levels of Inspection There are seven levels of inspection used in ANSI/ASQ Z1.4-2003: reduced inspection, normal inspection, tightened inspection, and four levels of special inspection. The special inspection levels should be used only when small sample sizes are necessary and large risks can be tolerated. When using ANSI/ASQ Z1.4-2003, a set of switching rules must be followed as to the use of reduced, normal, and tightened inspection. The following guidelines are taken from ANSI/ASQ Z1.4-2003: Initiation of inspection. Normal inspection level II will be used at the start of inspection unless otherwise directed by the responsible authority.



Continuation of inspection. Normal, tightened, or reduced inspection shall continue unchanged for each class of defect or defectives on successive lots or batches except where the following switching procedures require change. The switching procedures shall be applied to each class of defects or defectives independently. Switching procedures. Switching rules are shown in Figure 23.10. Normal to tightened. When normal inspection is in effect, tightened inspection shall be instituted when two out of five consecutive lots or batches have been rejected on original inspection (that is, ignoring resubmitted lots or batches for this procedure). Tightened to normal. When tightened inspection is in effect, normal inspection shall be instituted when five consecutive lots or batches have been considered acceptable on original inspection. Normal to reduced. When normal inspection is in effect, reduced inspection shall be instituted providing that all of the following conditions are satisfied: a. The preceding 10 lots or batches (or more), as indicated by the note on ANSI/ASQ Z1.4-2003 Table VIII, have been on normal inspection and none has been rejected on original inspection.

Part IV.C.2

Start • Preceding 10 lots accepted, with • Total nonconforming less than limit number (optional), and • Production steady, and • Approved by responsible authority

Reduced

• Two out of five consecutive lots not accepted

Normal

• Lot not accepted, or • Lot accepted but nonconformities found lie between Ac and Re of plan, or • Production irregular, or • Other conditions warrant

Tightened

• Five consecutive lots accepted • Five consecutive lots remain on tightened

• Discontinue inspection under Z1.4

Figure 23.10

Switching rules for normal, tightened, and reduced inspection.



b. The total number of defectives (or defects) in the sample from the preceding 10 lots or batches (or such other number as was used for condition (a) above) is equal to or less than the applicable number given in Table VIII of ANSI/ASQ Z1.4-2003. If double or multiple sampling is in use, all samples inspected should be included, not “first” samples only. c. Production is at a steady rate. d. Reduced inspection is considered desirable by the responsible authority. Reduced to normal. When reduced inspection is in effect, normal inspection shall be instituted if any of the following occur on original inspection: a. A lot or batch is rejected. b. A lot or batch is considered acceptable under reduced inspection but the sampling procedures terminated without either acceptance or rejection criteria having been met. In these circumstances, the lot or batch will be considered acceptable, but normal inspection will be reinstated starting with the new lot or batch. c. Production becomes irregular or delayed. d. Other conditions warrant that normal inspection shall be instituted.

Types of Sampling

Part IV.C.2

Discontinuation of inspection. If the cumulative number of lots not accepted in a sequence of consecutive lots on tightened inspection reaches five, the acceptance procedures of this standard shall be discontinued. Inspection under the provisions of this standard shall not be resumed until corrective action has been taken. Tightened inspection shall then be used as “normal to tightened” above.

ANSI/ASQ Z1.4-2003 allows for three types of sampling: 1. Single sampling 2. Double sampling 3. Multiple sampling The choice of the type of plan depends on many variables. Single sampling is the easiest to administer and perform but usually results in the largest average total inspection. Double sampling in ANSI/ASQ Z1.4-2003 results in a lower average total inspection than single sampling, but requires more decisions to be made, such as: • Accept the lot after first sample • Reject the lot after first sample



• Take a second sample • Accept the lot after second sample • Reject the lot after second sample Multiple sampling plans further reduce the average total inspection but also increase the number of decisions to be made. As many as seven samples may be required before a decision to accept or reject the lot can be made. This type of plan requires the most administration. A general procedure for selecting plans from ANSI/ASQ Z1.4-2003 is as follows: 1. Decide on an AQL. 2. Decide on the inspection level. 3. Determine the lot size. 4. Find the appropriate sample size code letter. See Table 1 from ANSI/ASQ Z1.4-2003. 5. Determine the type of sampling plan to be used: single, double, or multiple. 6. Using the selected AQL and sample size code letter, enter the appropriate table to find the desired plan to be used.

Part IV.C.2

7. Determine the normal, tightened, and reduced plans as required from the corresponding tables.

EXAMPLE 23.6 A lot of 1750 parts has been received and are to be checked to an AQL level of 1.5 percent. Determine the appropriate single, double, and multiple sampling plans for general inspection level II. Steps to define the plans are as follows: 4.1

Table I on page 10 of ANSI/ASQ Z1.4-2003 stipulates code letter K.

4.2

Normal inspection is applied. For code letter K, using Table II-A of ANSI/ ASQ Z1.4-2003 on page 11 of the standard, a sample of 125 is specified.

4.3

For double sampling, two samples of 80 may be required. Refer to Table III-A on page 14 of the standard.

4.4

For multiple sampling, at least two samples of 32 are required and it may take up to seven samples of 32 before an acceptance or rejection decision is made. Refer to Table IV-A on page 17 of the standard.

A breakdown of all three plans follows: Continued



Continued

Sampling plan

Sample(s) size

Ac

125

5

6

Single sampling

Re

Double sampling

First

80

2

5

Second

80

6

7

Multiple sampling

First

32

*

4

Second

32

1

5

Third

32

2

6 7

Fourth

32

3

Fifth

32

5

8

Sixth

32

7

9

Seventh

32

9

10

Ac = Acceptance number Re = Rejection number * Acceptance not permitted at this sample size.

Dodge-Romig Tables

Part IV.C.2

Dodge-Romig tables were designed as sampling plans to minimize average total inspection (ATI). These plans require an accurate estimate of the process average nonconforming in selection of the sampling plan to be used. The Dodge-Romig tables use the AOQL and LTPD values for plan selection, rather than AQL as in ANSI/ASQ Z1.4-2003. When the process average nonconforming is controlled to requirements, Dodge-Romig tables result in lower average total inspection, but rejection of lots and sorting tend to minimize the gains if process quality deteriorates. Note that if the process average nonconforming shows statistical control, acceptance sampling should not be used. The most economical course of action in this situation is either no inspection or 100% inspection (Deming 1982). See Duncan (1986) or Montgomery (2009b) for more details on Dodge-Romig tables.

VARIABLES SAMPLING PLANS Variables sampling plans use the actual measurements of sample products for decision making rather than classifying products as conforming or nonconforming, as in attribute sampling plans. Variables sampling plans are more complex in administration than attribute plans, thus they require more skill. They provide some benefits, however, over attribute plans. Two of these benefits are: 1. Equal protection to an attribute sampling plan with a much smaller sample size. There are several types of variables sampling plans in use, three of these being: (1) s known, (2) s unknown but can be estimated using sample standard deviation s, and (3) s unknown and the range R is used as an estimator. If an attribute sampling plan sample size is determined, the variables plans previously listed can be compared as a percentage to the attribute plan.



Plan

Sample size (percent)

Attribute

100

s unknown, range method

60

s unknown, s estimated from sample

40

s known

15

2. Variables sampling plans allow the determination of how close to nominal or a specification limit the process is performing. Attribute plans either accept or reject a lot; variables plans give information on how well or poorly the process is performing. Variables sampling plans, such as ANSI/ASQ Z1.9-2003, have some disadvantages and limitations: 1. Unlike attribute sampling plans, separate characteristics on the same parts will have different averages and dispersions, resulting in a separate sampling plan for each characteristic. 2. Variables plans are more complex in administration. 3. Variables gauging is generally more expensive than attribute gauging. In addition, for variables sampling plans it is assumed that the quality characteristic under study is normally distributed.

Part IV.C.2

ANSI/ASQ Z1.9-2003 ANSI/ASQ Z1.9-2003 is a revision of ANSI/ASQC Z1.9-1993 that includes changing the term acceptable quality level (AQL) to acceptable quality limit (AQL), changing the definition and explanation of AQL, and changing the discontinuation of inspection rule, as explained previously in terms of ANSI/ASQ Z1.4-2003. The most common standard for variables sampling plans is ANSI/ASQ Z1.9-2003, which has plans for: (1) variability known, (2) variability unknown— standard deviation method, and (3) variability unknown—range method. Using these methods, this sampling plan can be used to test for a single specification limit, a double (or bilateral) specification limit, estimation of the process average, and estimation of the dispersion of the parent population. As in ANSI/ASQ Z1.4-2003, several AQL levels are used and specific switching procedures for normal, reduced, or tightened inspection are followed. ANSI/ ASQ Z1.9-2003 allows for the same AQL value for each specification limit of double specification limit plans or the use of different AQL values for each specification limit. The AQL values are designated ML for the lower specification limit and MU for the upper specification limit. There are two forms used for every specification limit ANSI/ASQ Z1.9-2003 plan: form 1 and form 2. Form 1 provides only acceptance or rejection criteria, whereas form 2 estimates the percent below the lower specification limit and the percent above the upper specification limit. These percentages are compared to the AQL for acceptance/rejection criteria. Figure 23.11 summarizes the structure and organization of ANSI/ASQ Z1.9-2003.



Section A AQL conversion and inspection levels

Variability unknown Standard deviation method

Variability known

Single specification limits

k-method Procedure 1

Figure 23.11

M-method Procedure 2

Double specification limits

Variability unknown Range method

Process average estimation and criteria for reduced and tightened inspection

M-method Procedure 2

Structure and organization of ANSI/ASQ Z1.9-2003.

Part IV.C.2

There are 14 AQL levels used in ANSI/ASQ Z1.9-2003 that are consistent with the AQL levels used in ANSI/ASQ Z1.4-2003. Section A of ANSI/ASQ Z1.92003 contains both an AQL conversion table and a table for selecting the desired inspection level. Inspection level II should be used unless otherwise specified. See Section A7.1 of the standard for further information about levels. Table A-3 on page 7 of ANSI/ASQ Z1.9-2003 contains the OC curves for the sampling plans in Sections B, C, and D. Section B contains sampling plans used when the variability is unknown and the standard deviation method is used. Part I is used for a single specification limit, Part II is used for a double specification limit, and Part III is used for estimation of process average and criteria for reduced and tightened inspection. Section C contains sampling plans used when the variability is unknown and the range method is used. Parts I, II, and III are the same as Parts I, II, and III in Section B. Section D contains sampling plans used when variability is known. Parts I, II, and III are the same as Parts I, II, and III in Section B.

Variability Unknown—Standard Deviation Method In this section, a sampling plan is shown for the situation where the variability is not known and the standard deviation is estimated from the sample data. The sampling plan will be that for a double specification limit, and it is found in Section B of the standard with one AQL value for both upper and lower specification limits combined.



The acceptability criterion is based on comparing an estimated percent nonconforming to a maximum allowable percent nonconforming for the given AQL level. The estimated percent nonconforming is found in ANSI/ASQ Z1.9-2003 Table B-5. The quality indices for this sampling plan are: QU =

USL − X X − LSL and QL = s s

where USL = upper specification limit LSL = lower specification limit – X = sample mean s = estimate of lot standard deviation

Part IV.C.2

It should be noted that QL and Q U follow a standard normal distribution since the quality characteristic being measured is assumed to be normally distributed. The quality level of the lot is in terms of the lot percent defective. Three values are calculated: PU, PL, and p. PU is an estimate of conformance with the upper specification limit PL is an estimate of conformance with the lower specification limit, and p is the sum of PU and PL. The value of p is then compared with the maximum allowable percent defective. If p is less than or equal to M (ANSI/ASQ Z1.9-2003 Table B-5) or if either – Q U or QL is negative, the lot is rejected, since this would be the result of X lying beyond the specification limits. Example 23.7 illustrates the above procedure.

EXAMPLE 23.7 The minimum temperature of operation for a certain device is specified as 180°F. The maximum temperature is 209°F. A lot of 40 items is submitted for inspection. Inspection level IV, normal inspection with AQL = 1 percent, is to be used. ANSI/ASQ Z1.9-2003 Table A-2 (this table can be found on the CD-ROM accompanying this book), gives code letter D, which results in a sample size of five from ANSI/ASQ Z1.9-2003 Table B-3. The results of the five measurements in degrees Fahrenheit are as follows: 197, 188, 184, 205, 201. Determine if the lot meets acceptance criteria. Given: • Sample size, n = 5 • Upper specification limit, USL = 209 • Lower specification limit, LSL = 180 • From Table B of the ANSI/ASQ Z1.9-2003 standard, we find the maximum allowable percent nonconforming (M) to be M = 3.32% Let the random variable X represent the temperature of operation. The steps for calculating the percent nonconforming are as follows: Continued



Continued

1. Calculate the sample mean (see Chapter 32 for calculation of the sample mean): n

5

∑x ∑x i

x=

=

i=1

n

i

i=1

5

=

975 = 195 5

2. Calculate the sample standard deviation (see Chapter 32 for calculation of the sample standard deviation): n

s=

∑( x i=1

i

− x)

n−1

2

= 8 . 803

3. Calculate Q U: QU =

USL − x 209 − 195 = = 1 . 59 s 8 . 803

QL =

x − LSL 195 − 180 = = 1 . 70 s 8 . 803

4. Calculate Q L:

From Table B-5 of the ANSI/ASQ Z1.9-2003 standard, determine the percent nonconforming: • The percent above the upper specification limit with n = 5 and Q U = 1.59 is 2.19%. • The percent below the lower specification limit with n = 5 and Q L = 1.70 is 0.66%.

Part IV.C.2

The total percent nonconforming is then 2.19% + 0.66% = 2.85%. Therefore, since our percent nonconforming (2.85%) is less than the maximum allowable (3.32%), we conclude that the lot is acceptable.

SEQUENTIAL SAMPLING PLANS When tests are either destructive in nature or costly, it may be advantageous to use sequential sampling plans popularized by Wald (1973). These plans have the advantage of greatly reduced sample sizes while giving good protection. To determine a sequential sampling plan, the following parameters must be defined: a = producer’s risk AQL = acceptable quality level = p1 b = consumer’s risk RQL = rejectable (or unacceptable) quality level = p2; this is also referred to as limited quality level.



Number of nonconforming units

sn + h2 Reject zone

Continue testing

Accept zone sn – h1

Number of units inspected

Part IV.C.2

Figure 23.12

Decision areas for a sequential sampling plan.

The following example will use a = 0.05, AQL = 0.05, b = 0.1, RQL = 0.2. This results in a plan that will have a five percent chance of rejecting a lot that is five percent nonconforming and a 10 percent chance of accepting a lot that is 20 percent nonconforming. Figure 23.12 shows the accept, reject, and continue testing areas for a sequential sampling plan. The y-axis represents the number of nonconforming items in the sample and the x-axis represents the number of units inspected. The equations for the acceptance and rejection zone lines are: Reject zone line = sn + h2 Accept zone line = sn – h1 where: n = sample size h1 =

b ⎡ p2 ( 1 − p1 ) ⎤ log ⎢ ⎥ ⎢⎣ p1 ( 1 − p2 ) ⎥⎦

h2 =

a

s=

⎡ p ( 1 − p1 ) ⎤ log ⎢ 2 ⎥ ⎢⎣ p1 ( 1 − p2 ) ⎥⎦

log ⎡⎣( 1 − p1 ) / ( 1 − p2 ) ⎤⎦ ⎡ p ( 1 − p1 ) ⎤ log ⎢ 2 ⎥ ⎢⎣ p1 ( 1 − p2 ) ⎥⎦



⎡ (1 − b ) ⎤ a = log ⎢ ⎥ ⎢⎣ a ⎥⎦ ⎡ (1 − a ) ⎤ b = log ⎢ ⎥ ⎢⎣ b ⎥⎦

EXAMPLE 23.8 Assume that the following values are desired for a sequential sampling plan: a = 0.05, p 1 (AQL) = 0.05 b = 0.1, p 2 (RQL) = 0.2 Then: ⎡ 1 − 0 . 10 ⎤ a = log ⎢ = 1 . 2553 ⎣ 0 . 05 ⎥⎦ ⎡ 1 − 0 . 05 ⎤ b = log ⎢ = 0 . 9777 ⎣ 0 . 10 ⎥⎦

log [( 1 − 0 . 05) / ( 1 − 0 . 20 )]

= 0 . 1103 ⎡ 0 . 2 0 ( 1 − 0 . 05) ⎤ log ⎢ ⎥ ⎣ 0 . 05 ( 1 − 0 . 20 ) ⎦ 0 . 97 7 7 h1 = = 1 . 4448 ⎡ 0 . 20 ( 1 − 0 . 05) ⎤ log ⎢ ⎥ ⎣ 0 . 05 ( 1 − 0 . 20 ) ⎦ s=

1 . 2553 = 1 . 855 ⎡ 0 . 20 ( 1 − 0 . 05) ⎤ log ⎢ ⎥ ⎣ 0 . 05 ( 1 − 0 . 20 ) ⎦

Part IV.C.2

h2 =

Reject line = sn + h2 = ( 0 . 1103)( n) + 1 . 855 A ccept line = sn − h1 = ( 0 . 1103)( n) − 1 . 4448 Points for Accept and Reject Lines* n

Acceptance number

Rejection number

n

Acceptance number

Rejection number

1

A

B

14

0

4

2

A

B

20

0

5

3

A

3

24

1

5

4

A

3

40

2

7

5

A

3

50

4

8

6

A

3

*Acceptance values are rounded down to the nearest integer. Note A: Acceptance not possible when acceptance number is negative. Note B: Rejection not possible when rejection number is greater than sample number.

Continued



Continued

As can be seen by the preceding plan, rejecting the lot is not possible until the third sample unit and acceptance of the lot is withheld until the 14th sample unit.

Continuous Sampling Plans Many production processes do not produce lots, and thus lot-by-lot acceptance sampling plans discussed earlier cannot be applied. In cases such as these, continuous sampling plans are developed. In continuous sampling plans, 100 percent inspection and sampling inspection are alternately applied. The most recent standard for developing continuous sampling plans is MIL-STD-1235B. Continuous sampling plans are characterized by two parameters: i is called the clearance number or the number of conforming units under 100 percent inspection, and f is the ratio of the units inspected to the total number of units produced or passing through the inspection station.

Types of Continuous Sampling Plans There are two different standards for continuous sampling plans:

Part IV.C.2

1. Dodge’s continuous sampling plans. These include CSP-1 and CSP-2 sampling plans. These plans take AOQL (average outgoing quality limit) as an index. That is, for every AOQL value, there are different combinations of i and f. 2. MIL-STD-1235B. These plans are selected using a sample size code letter and an AQL value. The standard includes CSP-1, CSP-2, CSP-F, CSP-T, and CSP-V plans.

Dodge’s Continuous Sampling Plans These include CSP-1 and CSP-2 sampling plans. These plans take AOQL (average outgoing quality limit) as a quality index. Dodge’s CSP-1 continuous sampling plans operate as follows for a selected AOQL value: 1. Start with 100 percent inspection 2. When i (clearance number) consecutive number of units are found free from nonconformities, 100 percent inspection is then substituted with sampling inspection 2.1.

A fraction of f units is randomly selected and then inspected 2.1.1.

If one nonconformity is found, the 100 percent inspection procedure is restarted and the cycle is repeated

Dodge’s CSP-2 continuous sampling plans operate as follows for a selected AOQL value: 1. Start with 100 percent inspection



2. When i (clearance number) consecutive number of units are found free from nonconformities, 100 percent inspection is then substituted with sampling inspection 2.1.

A fraction of f units is randomly selected and then inspected

2.1.1.

If one nonconformity is found, the sampling inspection continues and the following procedure (2.1.2.) is initiated

2.1.2.

The number of conforming units (after finding the nonconformity) is counted 2.1.2.1. If i consecutive number are found free of nonconformities, sampling inspection continues 2.1.2.2.

If one nonconformity is found, 100 percent inspection is reinstated

MIL-STD-1235B

1. Start with 100 percent inspection

Part IV.C.2

This standard uses the same parameters, i and f, as previously defined. The standard includes CSP-1, CSP-2, CSP-F, CSP-T, and CSP-V plans. CSP-1 and CSP-2 plans operate in the same way as Dodge’s CSP-1 and CSP-2 plans, but they are selected based on a sample size code letter and an AQL value as a quality index. The sample size code letter is selected based on the number of units in the production interval. CSP-F plans work the same way as CSP-1 plans, providing alternate sequences of 100 percent and sampling inspection procedures, but the difference is that AOQL and the number of units in the production interval are used to characterize the plans in this case. Once AOQL and f values are selected, go to the corresponding table to read i, the clearance number. CSP-F is a single-level continuous sampling scheme. CSP-T plans provide the provision of reduced sampling frequency once the product shows superior quality. The CSP-T plan works as follows:

2. When i (clearance number) consecutive number of units are found free from nonconformities, 100 percent inspection is then substituted with sampling inspection 3. A fraction of f units is randomly selected and then inspected 3.1 If one nonconformity is found, the inspector reinstates 100 percent inspection 3.2 If the inspector finds i consecutive units free from nonconformities, the frequency f is reduced to f/2 3.2.1.

If one nonconformity is found, the inspector switches back to 100 percent inspection

3.2.2.

If the inspector finds i consecutive units free from nonconformities, the frequency f is reduced to f/4



3.2.2.1.

If one nonconformity is found, 100 percent inspection is reinstated

CSP-V plans work the same way as CSP-T plans but with reduced i instead of reduced f. The procedure is as follows: 1. Start with 100 percent inspection 2. When i (clearance number) consecutive number of units are found free from nonconformities, 100 percent inspection is then substituted with sampling inspection 3. A fraction of f units is randomly selected and then inspected 3.1. If one nonconformity is found, the inspector reinstates 100 percent inspection 3.2. If the inspector finds i consecutive units free from nonconformities, the inspection continues with inspecting the same fraction f 3.2.1.

If one nonconformity is found, the inspector switches back to 100 percent inspection

3.2.2.

If the inspector finds i/3, the sampling inspection continues with the same fraction f

3. SAMPLE INTEGRITY

Part IV.C.3

Identify the techniques for establishing and maintaining sample integrity. (Analyze) Body of Knowledge IV.C.3

Products are always at risk of contamination and misuse. Sample integrity is vital whenever sampling is done for any purpose, whether to go through a fitness program, for customer evaluation, or for destructive/nondestructive testing. In order to maintain sample integrity, carefully thought-out controls are necessary. Many people recall the murder trial of O. J. Simpson, where extremely complex and expensive DNA testing was challenged by the defense because the prosecution could not prove that the DNA sample was completely safe from any contamination at all times. While this is an extreme example, it highlights the importance of maintaining sample integrity.

Batch Control When products are created in batches (as opposed to discrete item production or continuous processes) it is necessary to keep records on all aspects of the batch. The concept of a batch includes mixing, heating, distilling, and comparable operations. A recipe is used, and documentation that the recipe was followed is vital



in all but the most trivial cases. A qualified operator must maintain a log or journal indicating the quantities and products (or identification) of each material that is inserted into the batch. The time each insertion is made is usually important, as well as the time that different inputs (heat, pressure, and so on) are applied to the batch. Tests may be required to verify that the batch has developed the needed properties over time. The results of such tests must be tightly linked to the physical batch and to all the other records. In some cases these details can be automated but often they must be recorded manually. When the batch is finished, it must be labeled with a separate identification code from other batches. The batch (lot) number must be printed or engraved on appropriate cartons, drums, jugs, pallets, and so on. A linkage between batch number and customer name is often necessary when the product is sold, so it can be tracked through the entire distribution chain.

Change Control

Part IV.C.3

Change control is a technique for dealing with relatively simple to moderately complex products to which minor changes are made that must be tracked. For example, such products as refrigerators and desktop computers may be changed slightly and new version numbers issued on the same model name/number. For warranty purposes, product repair, and replacement, it is necessary to record each time the product is changed. Engineers must decide when a change is required and how rapidly it is to be implemented. One priority scheme is to categorize the changes as emergency, priority, or routine. An emergency change is appropriate when a hazardous condition is discovered in the present version. In such cases, no time must be lost in correcting the deficiency. A priority change is called for if there is sound economic reason to make the change promptly, but life and property are not at risk. For example, a product upgrade that reduces power consumption or maintenance could be implemented as a priority change. The final category, routine, is for changes that must be made, but need not be rushed. These are often to accommodate newly designed parts or to allow the product to have slightly more functionality—but not enough to justify an entirely new model.

Configuration Control Configuration control is an extension of change control. The term configuration refers to how a complex product is composed of various units and subassemblies. In an evolving product with high research and development content, such as aerospace vehicles, defense weapons, and so on, the field version of the same unit of product gradually changes over time as new engines, new avionics, and new hydraulic systems are installed into existing units of product. In order to manage such ongoing field product modifications, a lot of effort must be put into configuration control systems. This is really an adaptation of materials resource planning techniques. Extensive documentation is mandatory for proper control. Usually both computerized database records and hard-copy backup records (often at multiple locations) are required.



Part IV.C.3

Two DOD standards address the subject of configuration control. DOD-STD480A addresses the big picture, specifying that the contractor analyze the impact of an engineering change proposal (ECP). DOD-STD-481 is more narrowly focused on how the customer of the material is to handle the change. A given contract may specify one or the other. A key principle of configuration control is to avoid changes in a given product model unless a clear and compelling benefit can be shown. Management must compare the downside of change—more complexity in the product line, more chance for confusion—against the claimed benefits: possibly reduced cost, increased performance, better safety, lower maintenance, and so on.



Chapter 24 D. Measurement and Test

THE MEASUREMENT PROCESS A measurement process is a repeated application of a test method using a measuring system. A test method includes requirements for a test apparatus and a welldefined procedure for using it to measure a physical property.

General Characteristics There are three general characteristics of a measurement process (Rashed and Hamouda 1974): 1. Realization of a test method. If a test method specifies use of a certain kind of test apparatus, a measurement process following the test method will utilize a particular version of such test apparatus. It will also involve a specific operator who is needed to carry out preparation of specimens and measurements.

Part IV.D.1

2. Realization of a system of causes. A system of causes is a collection of factors that may cause variability of measurements due to test apparatus, operator, test specimen, and other factors. Some causes may be explicitly involved in the test method. This realization of a system of causes defines the statistical universe of individual measurements. 3. Capability of statistical control. In a measurement, it is necessary to require the capability of statistical control. Capability of control means that either the measurements are obtained from an identifiable statistical universe or an orderly array of such universes, or if not, the physical causes preventing such identification may themselves be identified and, if desired, isolated and suppressed.

227



1. MEASUREMENT TOOLS

Select and describe appropriate uses of inspection tools such as gage blocks, calipers, micrometers, optical comparators, etc. (Analyze) Body of Knowledge IV.D.1

Part IV.D.1

Measurement is the process of evaluating a property or characteristic of an object and describing it with a numerical or nominal value. If the value is numerical, reflecting the extent of the characteristic, then the measurement is said to be on a quantitative scale and the actual property is referred to as a variable. Examples of variables inspection are measurements related to weight, length, temperature, and so on. If the value assigned to each unit is other than numerical, then the measurement is on a qualitative or classification scale and is referred to as an attribute. In most inspection situations involving nominal or attribute data, there are two possible nominal values: conforming (good) and nonconforming (defective). Each product unit is assigned one of these two labels according to inspection operation results. It is also possible to derive a numerical measure from a qualitative scale. This is achieved by calculating the fraction nonconforming (fraction defective) as the ratio between the number of units labeled as nonconforming and the total number of units inspected.

The Measuring System A measuring system should be able to provide accuracy capabilities that will assure the attainment of a reliable measurement. In general, the elements of a measuring system include the instrumentation, calibration standards, environmental influences, human operator limitations, and features of the workpiece or object being measured. Each of these elements may involve detailed studies of extended scope and thus fall beyond the purpose of this book. The design of measuring systems also involves proper analysis of cost-to-accuracy considerations (Darmody 1967). The functional design of measuring systems can include consideration of many approaches and employment of a variety of physical phenomena useful in establishing parametric variables from the measured quantity. In linear measuring systems, the basic function may be mechanical, optical, pneumatic, electronic, radiological, or combinations of these (Darmody 1967).

Controlling Product Quality In contrast to the rather imprecise measurements made and measurement standards used in everyday life, measurements and standards applied to manufactured parts must necessarily be extremely precise, since they must conform to definite geometric and aesthetic design specifications. The production of quality


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 24: D. Measurement and Test 229

products in any manufacturing operation requires an efficient and continuous testing program, and such programs have become increasingly important in recent years. Society has changed its attitudes, not only with respect to product safety and cost but also with respect to product reliability. Variations in product quality that were once accepted as the natural result of industrial systems are no longer tolerated. What is required today is the consistent extraction of the best technological quality available on a routine production basis. In this circumstance, testing serves two functions: to check on the performance of materials or components to obtain design data and to check on the conformity of a product to its design specifications. Testing of the latter type is commonly called inspection. Inspection to ensure and control product quality, as performed in industry, is of two kinds: 1. Visual inspection 2. Dimensional inspection Visual inspection, by far the more common of the two, involves visual examination by human operators for conformity to aesthetic requirements. Less common, but equally important and generally more difficult to perform adequately, is dimensional inspection. Visual and dimensional inspection are defined in the following paragraphs. Quality, of course, cannot be inspected into a product. Quality depends on engineering and manufacturing excellence, and inspection simply determines whether or not it exists. Better inspection is not the solution to large numbers of rejects. The solution must take the form of improvements in design or in the manufacturing process.

Visual inspection takes place, even if inadvertently, each time a part is handled during its manufacture. Parts such as bearing elements that have critical aesthetic requirements may also be given a final visual inspection once manufacture is complete. Visual inspection is concerned primarily with gross appearance—the detection of surface flaws and the recognition of patterns. These functions have, to date, attracted far less attention from developers of automatic inspection systems than have the functions associated with dimensional inspection. As a consequence, the human being currently is the most efficient general-purpose flaw-detection and pattern-recognition “instrument” available to the manufacturer. Human beings have highly developed sensing and data-processing faculties. Human operators are trainable and adaptive, although they are generally somewhat less reliable and experience more downtime than their automatic-equipment counterparts. Current research in artificial intelligence will surely cause this situation to change in the future.

Part IV.D.1

Visual Inspection

Dimensional Inspection Dimensional inspection refers to the measurement of lengths and angles and, in combination, of geometric shapes and may be accomplished automatically by



a machine or manually by an operator. Measurements that are taken while the product is still undergoing manufacture have a greater value than those applied to the finished product, since the former constitute process control whereas the latter are merely process verification. Obviously, it is more costly to correct or to scrap a bad product than it is to manufacture it properly in the first place. Because dimensional measurement is very important to every manufacturing operation, much effort has historically been expended toward improving the techniques and the instrumentation involved and toward refining the standards employed. The term “standard” has a dual meaning in the manufacturing environment. It is used to denote universally accepted specifications for devices, components, or processes, that ensure conformity and therefore interchangeability throughout a particular industry. Thus, one manufacturer’s screw will fit another’s nut, all makers of bricks will produce them in the same sizes, and all microscope objectives will fit all microscopes. As used in metrology, on the other hand, a standard provides a reference for assigning a numerical value to a measured quantity. The term “measurement” implies the comparison of an unknown with a known to determine the qualitative relationship between the two. Each basic, measurable quantity has associated with it an ultimate standard that embodies the definition of a particular unit. Working standards—those used in conjunction with the various measurementmaking instruments—are calibrated in terms of the particular unit definitions involved. Obviously, if measurements made at different locations are to be comparable, they must ultimately be traceable to the same standard.

Part IV.D.1

Selection of the Measuring Instrument Selection of a measuring tool or measuring instrument is based on several factors. In general, the Rule of Ten serves as a baseline for the selection process. The Rule of Ten states that inspection measurements should be better than the tolerance of a dimension by a factor of 10 and calibration standards should be better than the inspection instrument by a factor of 10. Once this rule is implemented, candidate instruments need to be evaluated based on the following criteria: • Accuracy and precision • Repeatability • Sensitivity • Resolution • Stability and consistency • Part or workpiece material • Shape and dimensions of the part being measured • Capabilities of the metrology laboratory



Measurement Technology The following is a review of the different measurement instruments and technologies employed in common measurement practices such as length and angle measurement, surface texture measurement, and measurement of out-of-roundness.

Length and Angle Measurements The standard environmental conditions for length measurements include a temperature of 68°F (20°C) and a barometric pressure of 760 mm Hg. Because these conditions are assumed for all precision dimensional measurements, dimensional metrology laboratories are temperature-controlled as nearly as is practical to 68°F, and thermal expansion corrections are made for any deviations that may occur. It is seldom necessary to correct for thermal expansion to achieve the accuracy required in industrial movement. Since the majority of precision parts, like the masters against which they are measured, are made of steel, it is generally safe to assume that their thermal expansion coefficients are identical and that no temperature correction need be made. Temperature corrections are also unnecessary when angles alone are measured, since a uniform temperature change cannot change the size of an angle. This will definitely change with the introduction of new materials.

Instrumentation for Dimensional Measurements Dimensional (or linear) measuring instruments are used to measure length. They are of two types: 1. Absolute instruments

Absolute instruments have their working standards built in and thus require no mastering; they are generally used for long-range measurements. Comparators are short-range devices that measure deviations between a working master and a given part. The yardstick is a crude example of the first type, and the dial indicator is an example of the second. Table 24.1 details typical units, standards, and instruments for length and angle measurements. Measuring instruments range from very basic tools to more sophisticated measuring machines, such as coordinate measuring machines and laser scanners. This section contains a review of those basic measuring tools that are commonly used for many applications. These basic measuring equipment and tools include:

Part IV.D.1

2. Comparative instruments, or comparators

• Surface plates • Micrometers • Verniers



Table 24.1

Typical standards and instrumentation for industrial length and angle measurements. Length measurements

Angle measurements

Unit of measurement

Meter

Radian

Ultimate standard

Speed of light

Circle

Single-valued working standards

Length gage blocks

Angle gage blocks

Many-valued working standards

Line scales, step bars

Optical polygons, serrated-type index tables

Displacement-measuring instruments

Interferometers

Autocollimators

• Comparators • Dial indicators • Gage blocks • Ring, plug, and snap gages

Part IV.D.1

Basic Linear Measuring Instruments Most of the basic or general-purpose linear measuring instruments are typified by the use of steel rulers, vernier calipers, and micrometer calipers. Steel rulers are commonly used for linear measurements, in which the ends of a dimension being measured are aligned with graduations of the scale from which the length is read directly. A specialized type of steel ruler is the depth ruler that is used for measuring holes, slots, and so on. Vernier calipers are used for inside or outside linear measurement. Other types of verniers include digital reading calipers that provide LCD readouts in micrometers (mm) or microinches (min) and vernier height gages that can measure external, internal, and distance dimensions, as well as perpendicularity, flatness, straightness, centers, and diameters. Micrometers come in various types. The measuring element of a micrometer consists of a fixed anvil and a spindle that moves lengthwise as it turns. Vernier micrometer calipers use a vernier scale on the sleeve. Digital micrometers use digital readouts to make readings faster and easier. Indicating micrometers have a built-in dial indicator to provide a positive indication of measuring pressure applied.

Angular Measuring Devices Angular measurements use the degree as the standard unit. Angular measuring devices range from simple tools such as protractors, bevel protractors, and squares to sine bars and dividing heads. The protractor reads directly in degrees. A bevel protractor utilizes a vernier scale that shows angles as small as five or less minutes.



The sine bar is a more precise device for precision measuring and checking of angles. It consists of an accurately ground flat steel straight edge with precisely affixed round buttons that are a known distance apart and of identical diameters. Dividing heads are either optical or mechanical devices that often are used for the circular measurement of angular spacing, common in machine tool operations.

Layout and Locating Devices Surface plates provide a relatively accurate surface plane from which measurements can be made. Surface plates may employ a cast iron or granite surface. Granite surface plates provide better hardness, resistance to corrosion, nonmagnetic characteristics, and less response to temperature changes than cast iron surface plates.

Gages

Part IV.D.1

Gages are used to determine the conformance or nonconformance of a dimension to required specifications without attempting actual measurements. Typical common functional gages are classified according to their use for checking outside dimensions, inside dimensions, or special features. Ring and snap gages are used for checking outside dimensions, plug gages are used for checking inside dimensions, and other gages are used for checking special features like tapers, threads, and splines. They normally provide a go/no-go decision on part specifications. Go/no-go gages (also known as limit gages) are made to sizes essentially identical with the design specification limits of the dimension to be inspected. If a specific gage can properly mate with a part, then the part can be assembled with another part whose physical boundaries do not exceed those of the gage. Consequently, the part is acceptable for assembly. Limit gages designed to identify this condition are called go gages. The “go” end of a go/no-go gage contains the reverse physical replica of the dimension inspected at the maximum material condition (minimum size for interior features, maximum size for exterior features). The maximum material condition produces the minimum clearance required for assembly. The “no-go” end is designed to detect conditions of excessive clearance. It contains the reverse physical replica of the dimension inspected at its minimum material condition. A part will not mate with a no-go gage unless the actual condition of the part feature is below the specified minimum. Thus, if the no-go gage mates with the part, then the part dimension is incorrect and the part should be rejected. In practice, go/no-go gages are used together and often appear at opposite ends of an inspection instrument. An acceptable part should mate with the go end but should not mate with the no-go end. Parts that mate with neither or both ends do not meet design specifications and should be rejected. Most methods of inspection by attributes, other than gauging, are largely subjective and depend on the ability of human inspectors to make the right decision. In many cases, inspection by attributes involves visual characteristics, such as color, shape, smoothness, and other visual defects.



Dial Indicators Dial indicators magnify the dimension deviation from a standard to which the gage is set. Dial indicators are used for many kinds of checking and gauging operations, checking machines and tools, verifying alignments, and cutter runout. Some indicators employ mechanical mechanisms for their operation and others come with a digital readout.

Comparators Comparators normally employ dial indicators for their operation and come in different varieties: mechanical, optical, electronic, and pneumatic. Optical projectors, also known as optical comparators, employ a system in which light rays are directed against the object and then reflected back through a projection lens onto a screen. The projections are large enough to accurately measure small configurations of objects.

Gage Blocks Gage blocks are the practical standards for length in the manufacturing industry. They are rectangular, square, or round blocks of steel, carbide, or ceramic materials. Each has two faces that are flat, level, and parallel with an accuracy and length grade, depending on the application.

Part IV.D.1

Surface Texture Measurement Surface metrology may be broadly defined as the measurement of the difference between what the surface actually is and what it is intended to be. It is treated separately from length measurement, which is concerned with the relationship of two surfaces on a workpiece. Surface measurement, however, is involved with the relationship of a surface on the workpiece to a reference that is not actually on the workpiece. The most common aspect of surface metrology is the measurement of surface roughness as an average deviation from a mean center line (Bosch 1984).

Numerical Assessment of the Surface Of all the methods used for the numerical assessment of the surface, the following are the most widely used (Reason 1960): 1. Peak-to-valley measure 2. Mean-line measures (center line average [CLA] and root mean square [RMS]) 3. Crest-line measures 4. Envelope method, in which the crest line should be defined as the locus of the center of a circle or defined radius rolling across the surface, the locus being displaced toward the surface until it contacts the crests



The international standard for the assessment of surface texture, ISO/R468, defines three parameters: Ra (CLA), Rz, and Rmax, all measured relative to a straight mean line. These parameters are shown in Figure 24.1 and can be defined as (Spragg 1976): 1. Ra (center line average) value is the arithmetic mean of the departures of a profile from the mean line. It is normally determined as the mean result of several consecutive sample lengths L. 2. Rz (ten-point height) is the average distance between the five height peaks and five deepest valleys within the sampling length and measured perpendicular to it. 3. Rmax is the maximum peak-to-valley height within the sampling length.

+y

–y L I Ra = _ L

L

y dL ° ||

R3 R2R5 R4

RZ =

(R1 + R3 +... R9) – (R2 + R4 +... R10) 5

R7R6

R8 R9 R10

Part IV.D.1

R1

Rmax. +y

–y L

Figure 24.1

ISO/R468 surface roughness parameters.



Other parameters of surface roughness are shown in Figure 24.2. They are defined as follows (Machinability Data Center 1980): 1. Rtm is the average value of Rmax’s for five consecutive sampling lengths. 2. Rp is the maximum profile height from the mean line within the sampling length. Rpm is the mean value of Rp’s determined over five sampling lengths. 3. PC (peak count) is the number of peak/valley pairs per inch projecting through a band of width b centered about the mean line.

Rmax1

Rt

Rmax2

L

Rmax3

L

L Assessment length

Rmax4

Rmax5

L

L

5

Rtm =

∑R

max i

i =1

5

+ Rmax 2 + Rmax 3 + Rmax 4 + Rmax 5 R = max 1 5

Part IV.D.1

Rp1

L

Rp2

Rp3

L

L

Rp5

Rp4

L

L

5

R pm = = 1 count

∑R

pi

i =1

5 Rp1 + Rp 2 + Rp 3 + Rp 4 + Rp 5 5 1 count

1 count

Mean line

Figure 24.2 Other parameters of surface roughness.



The most common method of surface measurement is to move a stylus over the surface and measure an average electrical signal produced by a transducer attached to the stylus. Other means used less frequently include stylus profiling, where a chart record is produced instead of an average number, reflectance meters, pneumatics, and optical interference. The stylus averaging unit is the most common because it is fast, repeatable, quite easy to interpret, and relatively inexpensive (Bosch 1984).

Measurement of Roundness Geometrically, a part can be said to be round in a given cross section if there exists within the section a point from which all points on the periphery are equidistant. In practice, however, the radius of nominally round parts tends to vary from point to point. Thus, the problem found by the metrologist is one of displaying and assessing these variations, and correctly interpreting the results (Bosch 1984).

Roundness Measurement Methods

a Figure 24.3

Part IV.D.1

Although many methods have been used for roundness measurement, only those that provide valid radial-deviation data lend themselves to standardization and consistent, accurate measurement of all out-of-roundness conditions. For this reason, current industry, national, and international standards primarily cover measurements taken with precision spindle-type instruments with the data recorded on a polar chart. Precision spindle instruments include those in which the spindle supports and rotates the part with the gage tip remaining stationary, and those in which the spindle rotates the gage tip about the part, which remains stationary. Figure 24.3 illustrates these two types of out-of-roundness measurement (Drews 1978).

b

Two types of roundness-measuring instruments: (a) rotating table, (b) rotating workpiece.



The center of rotation of the precision spindle and the indicator gage tip provides a master radius to which all the radii of a cross section profile of the part are compared. It is necessary that the center of the part cross section and the spindle axis be adjusted to be concentric within narrow limits. The variations of the cross section radii from the master radius are usually recorded in a highly magnified form on a polar chart. Because the out-of-roundness value is defined as the difference between the largest and smallest radius that will just contain the measured profile, these radii must be measured from a specified center. The choice of these reference circles is arbitrary, but is chosen to fulfill some functional requirements. As shown in Figure 24.4, there are four ways in which a center can be chosen (Drews 1978): 1. Minimum radial separation (MRS) (also known as minimum zone circle [MZC]) 2. Least squares circle (LSC) 3. Maximum inscribed circle (MIC)

Part IV.D.1

4. Minimum circumscribed circle (MCC) The magnified profile produced on the polar chart is evaluated by two concentric circles that just contain the profile when centered in accordance with the minimum-radial-separation center criteria. Other center criteria can be specified. For example, the concentric circles could be engraved on a transparent overlay (a more common method). The out-of-roundness value is the separation of the two concentric circles divided by the magnification setting of the instrument. The polar chart clearly shows the number and magnitude of the roundness deviations. There are many advantages to the precision spindle methods. Accurate measurements of all types of out-of-roundness are possible and a permanent polar chart, which is easily interpreted, is provided. It is also the most accurate method of measurement available. With proper equipment, accuracies of one microinch are attainable. In addition to roundness, the equipment also permits ultraprecise measurement of centricity, squareness, flatness, and other related geometric partfeature characteristics.

Coordinate Measuring Machines Coordinate measuring machines have become a primary means of dimensional quality control for manufactured parts of complex form, where the volume of production does not warrant the development of functional gauging. The advent of increasingly inexpensive computing power and more fully integrated manufacturing systems will continue to expand the use of these machines into an even larger role in the overall quality assurance of manufactured parts. Coordinate measuring machines (CMMs) can most easily be defined as physical representations of a three-dimensional rectilinear coordinate system. Coordinate measuring machines now represent a significant fraction of the measuring equipment used for defining the geometry of different-shaped workpieces. Most dimensional characteristics of many parts can be measured within minutes with these machines. Similar measurements would take hours using older measuring equipment and procedures. Besides flexibility and speed, coordinate measuring machines have several additional advantages:



Profile graph Reference circle

Minimum radial separation (MRS or MZC) Two concentric circles are chosen so as to have the least radial separation and yet contain between them all of the polar trace. This radial separation is the measure of the out-of-roundness value. The radial difference between concentric circles determined by this method is numerically unique, in that by definition a smaller value cannot exist.

Least squares circle (LSC) A theoretical circle is located with the polar profile such that the sum of the squares of the radial ordinated between the circle and the profile is a minimum. The out-of-roundness value would be determined by the sum of the maximum inward and maximum outward ordinates divided by the proper chart amplification factor.

Minimum circumscribed circle (MCC)

This procedure determines the center of the polar profile by the center of the largest circle that can be fitted inside the profile. From this circle the maximum outward departure of the profile denotes the out-of-roundness.

The profile center is determined by the smallest circle that will just contain the measured profile. From the circle, the maximum inward departure of the profile can be measured; this maximum departure is the out-of-roundness.

Figure 24.4

Part IV.D.1

Maximum inscribed circle (MIC)

Four ways by which a center may be chosen.

1. Different features of a part can be measured in one setup. This eliminates errors introduced due to setup changes. 2. All CMM measurements are taken from one geometrically fixed measuring system, eliminating the accumulation of errors resulting from using functional gauging and transfer techniques. 3. The use of digital readouts eliminates the necessity for the interpretation of readings, such as with the dial or vernier-type measuring scales.



4. Most CMMs have automatic data recording, which minimizes operator influence. 5. Part alignment and setup procedures are greatly simplified by using software supplied with computer-assisted CMMs. This minimizes the setup time for measurement. 6. Data can be automatically saved for further analysis.

Coordinate Measuring Machines Classification Although coordinate measuring machines can be thought of as representations of a simple rectilinear coordinate system for measuring the dimensions of differentshaped workpieces, they naturally are constructed in many different configurations, all of which offer different advantages. CMMs provide means for locating and recording the coordinate location of points in their measuring volumes. Traditional coordinate measuring machines are classified according to their configurations, as follows (ASME 1985):

Part IV.D.1

1. Cantilever configuration, in which the probe is attached to a vertical machine ram (z-axis) moving on a mutually perpendicular overhang beam (y-axis) that moves along a mutually perpendicular rail (x-axis). Cantilever configuration is limited to small and medium-sized machines. It provides for easy operator access and the possibility of measuring parts longer than the machine table. 2. Bridge-type configuration, in which a horizontal beam moves along the x-axis, carrying the carriage that provides the y-motion. In other configurations, the horizontal beam (bridge structure) is rigidly attached to the machine base and the machine table moves along the x-axis. This is called fixed bridge configuration. A bridge-type coordinate measuring machine provides more rigid construction, which in turn provides better accuracy. The presence of the bridge on the machine table makes it a little more difficult to load large parts. 3. Column-type configuration, in which a moving table and saddle arrangement provides the x and y motions and the machine ram (z-axis) moves vertically relative to the machine table. 4. Horizontal-arm configuration features a horizontal probe ram (z-axis) moving horizontally relative to a column (y-axis), which moves in a mutually perpendicular motion (x-axis) along the machine base. This configuration provides the possibility for measuring large parts. Other arrangements of horizontal-arm configuration feature a fixed horizontal-arm configuration in which the probe is attached and moving vertically (y-axis) relative to a column that slides along the machine base in the x-direction. The machine table moves in a mutually perpendicular motion (z-axis) relative to the column. 5. Gantry-type configuration comprises a vertical ram (z-axis) moving vertically relative to a horizontal beam (x-axis), which in turn moves along two rails (y-axis) mounted on the floor. This configuration provides easy access and allows the measurement of large components.



6. L-shaped bridge configuration comprises a ram (z-axis) moving vertically relative to a carriage (x-axis), which moves horizontally relative to an L-shaped bridge moving in the y-direction. Figure 24.5 shows CMM types according to this classification. The most advanced configuration, that of the ring-bridge, is not illustrated.

y y z

x

x

z

Fixed bridge

Cantilever

x z z

y

x y

Moving bridge

Column

Part IV.D.1

y

z z y x

Fixed horizontal arm

x

Moving horizontal arm

z

y x

y x

z

Gantry

Figure 24.5

L-shaped bridge

Coordinate measuring machine classifications.



In addition to classifying coordinate measuring machines according to their physical configuration, they can also be classified according to their mode of operation: manually oriented, computer-assisted, or direct computer-controlled. With manual machines, the operator moves the probe along the machine’s axes to establish and manually record the measurement values that are provided by digital readouts. In some machines, digital printout devices are used. Computer-assisted coordinate measuring machines can be either manually positioned (free-floating mode) by moving the probe to measurement locations, or manually driven by providing power-operated motions under the control of the operator. In either case, data processing is accomplished by a computer. Some computer-assisted CMMs can perform some or all of the following functions: inch to metric conversion, automatic compensation for misalignment, storing of premeasured parameters and measurement sequences, data recording, means for disengagement of the power drive to allow manual adjustments and manipulations of the machine motions, and geometric and analytical evaluations. Direct computer-controlled CMMs use a computer to control all machine motions and measuring routines and to perform most of the routinely required data processing. These machines are operated in much the same way as CNC machine tools. Both control and measuring cycles are under program control. Offline programming capability is also available. The effective use of computers for CMM applications is a principal feature differentiating available CMM systems. The value of a measurement system depends a great deal on the sophistication and ease of use of the associated software and its functional capabilities. The functional capabilities of a CMM software package depend on the number and types of application programs available.

Part IV.D.2

2. DESTRUCTIVE AND NONDESTRUCTIVE TESTS

Distinguish between destructive and nondestructive measurement test methods and apply them appropriately. (Analyze) Body of Knowledge IV.D .2

Testing involves evaluation of product conformance to certain design or production requirements. In addition, the output of testing can be used to evaluate new designs during product development, to define a product’s potential failure causes, in product reliability evaluation, and so on.

Testing versus Inspection Inspection is the evaluation of product quality by comparing the results of measuring one or several product characteristics against applicable standards. From this definition it is evident that the inspection function involves a number of tasks:



1. Measurement, which could be on a qualitative or quantitative scale. The objective is to make a judgment about the product’s conformance to specifications. 2. Comparison of the measurement results to specific standards that reflect the intended use of the product by the customer and the various production costs. If the product is found to be nonconforming, a decision as to whether nonconforming products are fit for use may be reached. 3. Decision making regarding the disposition of the unit inspected and, under sampling inspection, regarding the lot from which the sample was drawn. 4. Corrective action(s) in order to improve the quality of the product and/or process based on the aggregate results of inspection over a number of units. Testing is also carried out to determine the conformity of a product by comparing the results of measuring one or several product characteristics against applicable standards. It involves similar tasks as inspection. The difference is that testing can be performed on a part, a product, a subassembly, or an assembly, while inspection is typically performed on a component or a part of a product.

Testing versus Gauging

Destructive and Nondestructive Testing

Part IV.D.2

Two terms normally associated with inspection are gauging and testing. Gauging determines product conformance with specifications with the aid of measuring instruments such as calipers, micrometers, templates, and other mechanical, optical, and electronic devices. Testing refers to the determination of the capability of an item to meet specified requirements by subjecting it to a set of physical, chemical, environmental, or other operating conditions and actions similar to or more severe than those expected under normal use.

Testing might be destructive or nondestructive. In testing, the product is subjected to measuring procedures that render its usefulness to the customer. Gauging, however, is the more common form of inspection and is less costly; this operation has no effect on the product’s service capability. Of course, certain product characteristics, mainly those related to failure modes, may only be observed and measured by exposing the product to conditions beyond its designed limits, such as determining the maximum current that an electronic component can carry or the maximum tensile force that a mechanical part can withstand. Most of these procedures normally are destructive testing procedures and may be performed in cases where mandatory requirements are to be met. Nondestructive testing (NDT) of products usually is performed by subjecting the product to tests such as eddy current, ultrasonic resonance, or x-ray testing. Nondestructive Testing Techniques. Screening or 100 percent inspection can not be used when the product is subjected to a destructive testing procedure or the



time involved in performing inspection is too long. Another constraint is that the cost of inspection may be too high to justify the economics of inspection. NDT techniques are more common for automated inspection or 100 percent inspection. The most common NDT techniques include: • Eddy current testing involves the application of an AC current passing through a coil that is placed near the surface of the part to be inspected. Thus, its application is limited to conducting materials and the test results are made by comparison. • Ultrasonic testing normally is used to check for surface defects that cause deflection of an ultrasonic wave directed on the part surface, thus giving an indication of the presence of a surface defect. For ultrasonic testing, reference standards are required. • X-ray techniques cause the internal characteristics of the part to be displayed and thus provide information about the presence of defects, cracks, or other impurities. • Liquid penetration is more common for detecting defects on the part surface. It is used for different part configurations and, unlike magnetic particle testing, it can be used for nonmagnetic materials. However, liquid penetration can not be used to locate subsurface discontinuities.

Part IV.D.2

• Magnetic particle testing is used when the part material can be magnetized. Discovery of part defects, like cracks or discontinuities, can then be detected by the presence of paring magnetic fields. Magnetic particle testing is limited to parts made of iron, steel, or allied materials. Other common NDT techniques include the application of some phenomenon, such as thermal, chemical, holographic inteferometry (employing interference patterns for checking surface displacements), or optical phenomena. These are used for special testing procedures and often are too expensive to be widely applied.



Chapter 25 E. Metrology

Identify, describe, and apply metrology techniques such as calibration systems, traceability to calibration standards, measurement error and its sources, and control and maintenance of measurement standards and devices. (Analyze) Body of Knowledge IV.E

INTRODUCTION AND SCOPE

Part IV.E

The science of precision measurement, usually referred to as metrology, encompasses all scientific disciplines. The word “metrology” is derived from two Greek words: metro, meaning measurement, and logy, meaning science. The term is used in a more restricted sense to mean that portion of measurement science that is often used to provide, maintain, and disseminate a consistent set of units, to provide support for the enforcement of equity in trade by weights and measurement laws, or to provide data for quality control in manufacturing (Simpson 1981).

CONTEXT OF MEASUREMENTS A measurement is a series of manipulations of physical objects or systems according to a defined protocol that results in a number. The number is purported to uniquely represent the magnitude (or intensity) of a certain property, which depends on the properties of the test object. This number is acquired to form the basis of a decision affecting some human goal or satisfying some human object need, the satisfaction of which depends on the properties of the test subject. These needs or goals can usefully be viewed as requiring three general classes of measurements (Simpson 1981): 1. Technical. This class includes those measurements made to assure dimensional compatibility, conformation to design specifications necessary for proper function, or, in general, all measurements made to ensure fitness for intended use of some object. 245



2. Legal. This class includes those measurements made to ensure compliance with a law or regulation. This class is the concern of weights and measures bodies, regulators, and those who must comply with regulations. The measurements are identical in kind with those of technical metrology but usually are embedded in a much more formal structure. Legal metrology is more prevalent in Europe than in the United States, although this is changing. 3. Scientific. This class includes those measurements made to validate theories of the nature of the universe or to suggest new theories. These measurements, which can be called scientific metrology (properly the domain of experimental physics), present special problems.

Standards of Measurement The National Institute of Standards and Technology (NIST) is the American custodian of the standards of measurement. It was established by an act of Congress in 1901 although the need for such a body had been noted by the founders of the Constitution. NIST’s two main campuses are in Gaithersburg, Maryland, and Boulder, Colorado, where research into the phenomenon of measurement, the properties of materials, and calibration of the reference standards submitted by laboratories from throughout the United States are carried out. The following is a generalization of the echelons of standards in the national measurement system (Rice 1986):

Part IV.E

• National standards. Include prototype and natural phenomena of SI (Systems International, the worldwide system of weight and measures standards) base units and reference and working standards for derived and other units. • Metrology standards. Reference standards of industrial or governmental laboratories. • Calibration standards. Working standards of industrial or governmental laboratories. Frequently, there are various levels within these echelons (Mack 1976). In order to maintain accuracy, standards in a vast industrial complex must be traceable to a single source, usually the country’s national standards. Since the national laboratories of well-developed countries maintain close connections with the International Bureau of Weights and Measures, there is assurance that items manufactured to identical dimensions in different countries will be compatible (McNish 1967). Application of precise measurement has increased so much during the past few years that it is no longer practical for a single national laboratory to perform directly all the calibrations and standardization required by a large country with a high technical development. This has led to the establishment of a considerable number of standardizing laboratories in industry and in various branches of the state and national governments (see Figure 25.1). In order that results of calibrations be uniform, the standardizing laboratories must maintain close rapport with the national laboratory. This is facilitated by the use of uniform terminology in discussing standards (McNish 1967).


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 25: E. Metrology 247

National standards

National reference standards

Working standards

Interlaboratory standards

Laboratory reference standards

Working standards

Reference or working standards of lower order Figure 25.1

Classification of standards.

Concern with Standards

1. Definitions of base units 2. Physical artifacts

Part IV.E

The term standard includes three distinct areas, all of which are of importance in metrology (NIST 1981):

3. Paper standards

Definitions of Base Units The definitions of the base units of measurement form a reference from which all other units can be derived. These base units, together with two supplementary units related to angle measurement that are necessary to specify a complete system of units, are listed in Table 25.1. Table 25.2 gives the definitions of all the SI units listed in Table 25.1. The definitions can also be found at the NIST Web site at http://physics.nist.gov/Pubs/SP811/appenA.html. A current chart showing the relationships of all the SI units to which names have been assigned can also be found at the NIST Web site at http://ts.nist.gov/WeightsAndMeasures/Metric/ pub814.cfm. All of the SI units listed in Tables 25.1 and 25.2 are defined in terms of experiments that can be performed in any suitably equipped laboratory, except for



Table 25.1

Base units of the international system.

Quantity

Name

Length

Meter

Mass

Kilogram

Time

Second

Electric current

Ampere

Thermodynamic temperature

Kelvin

Amount of substance

Mole

Luminous intensity

Candela

Plane angle*

Radian

Solid angle*

Steradian

*Supplementary units

Part IV.E

Table 25.2

Definitions of the SI base units.

Unit

Definition

meter–m

The distance traveled by light in a vacuum during a time interval of 1/299, 792, 458 of a second.

kilogram–kg

A cylinder of platinum-iridium alloy kept by the International Bureau of Weights and Measures at Paris. A duplicate in the custody of the National Institute of Standards and Technology serves as the mass standard for the United States.

second–s

The duration of 9, 192, 631, 770 cycles of the radiation associated with a specified transition of the cesium-133 atom. It is realized by tuning an oscillator to the resonance frequency of cesium-133 atoms as they pass through a system of magnets and a resonant cavity into a detector.

Ampere–A

That current which, if maintained in each of two long parallel wires separated by one meter in free space, would produce a force between the two wires (due to their magnetic fields) of 2 × 10 –7 newton for each meter of length.

Kelvin–K

The fraction 1/273.16 of the thermodynamic temperature of the triple point of water. The temperature 0 K is called absolute zero.

mole–mol

The amount of substance of a system that contains as many elementary entities as there are atoms in 0.012 kilogram of carbon-12.

candela–cd

The luminous intensity, in a given direction, of a source that emits monochromatic radiation of frequency 540 × 1012 (Hz) and that has a radiant intensity in that direction of 1/683 watt per steradian.

radian–rad

The plane angle with its vertex as the center of a circle that is subtended by an arc equal in length to the radius.

steradian–sr

The solid angle with its vertex at the center of a sphere that is subtended by the area of the spherical surface equal to that of a square with sides equal in length to the radius.

Source: NIST Special Publication 304A, August 1981 (used with permission).



Part IV.E

the definition of the unit mass, the kilogram. The kilogram is the only base unit defined in terms of a physical artifact. It must therefore be carefully preserved and protected, and the unit can only be disseminated by direct comparisons with the defining artifact. The kilogram is the mass of the International Prototype of the Kilogram, which is kept at the International Bureau of Weights and Measures near Paris, France. The standard for angle measurements is present in the form of the circle, and units of angle are defined in terms of this standard. Thus, one degree is the angle that subtends 1/360 of the circumference of a circle, and one radian is the angle that subtends 1/(2p) times the circumference. Measurements of length are man-defined and man-made. Until 1960, the meter was defined as the distance, under certain specified environmental conditions, between two lines engraved on the neutral axis of the International Prototype Meter, a bar of 90 percent platinum/10 percent iridium alloy, which is preserved at the International Bureau of Weights and Measures; the Prototype Meter No. 27, whose length was known in terms of the international prototype, served as a standard for the United States. This method of defining the meter length was not entirely satisfactory, since it required periodic recalibration of the various national standards in terms of the international standard. In 1960, the Eleventh General Conference on Weights and Measures redefined the meter as a length equal to 1,650,763.73 wavelengths, in a vacuum, of the orange-red radiation corresponding to the transition between the 2p10 and 5d5 levels of the krypton-86 atom. The meter so defined is identical to that previously defined, within the limits of accuracy of the various measurements involved. The new definition provided a standard for length measurement that was based on an unchanging physical constant that could be reproduced in any properly equipped laboratory in the world. The inch is defined as 0.0254 meters. The definition of the meter was again changed in 1975 by the General Conference of Weights and Measures. The current definition of the meter is the length of a path traveled by light in a vacuum during a time interval of 1/299,792,458 of a second. This definition for the meter thus defines the speed of light to be exactly 299,792,458 meters/second, and with this definition the meter could be realized from the wavelength of any coherent optical source whose frequency is known and the wavelength is the speed of light divided by the frequency.

Physical Artifacts Physical artifacts are manufactured with high precision to embody a particular quantity, dimension, or feature. These include such items as gage blocks for length, standard resistors for electrical resistance, standards for cell voltage, and so on. This class of artifacts also includes high-precision analog measurement instruments that can be used as masters for reference, such as mercury in glass thermometers and dead weight testers for pressure.

Paper Standards Paper standards are the many documents published by various technical societies and standards-writing organizations that contain specifications or generally accepted methods for making measurements.



CONCEPTS IN METROLOGY A fundamental role of the metrology and calibration process is to assign accuracy or uncertainty statements to a measurement. This can be achieved by defining characteristics of measuring system elements as well as equipment limitations.

Error in Measurement Error in measurement is the difference between the indicated value and the true value of a measured quantity. The true value of a quantity to be measured is seldom known. Errors are classified as: 1. Random errors

Part IV.E

2. Systematic errors Random errors are accidental in nature. They fluctuate in a way that cannot be predicted from the detailed employment of the measuring system or from knowledge of its functioning. Sources of error such as hysteresis, ambient influences, or variations in the workpiece are typical but not all-inclusive in the random category. Systematic errors are those not usually detected by repetition of the measurement operations. An error resulting from either faulty calibration of a local standard or a defect in contact configuration of an internal measuring system is typical but not completely inclusive in the systematic class of errors (Darmody 1967). It is important to know all the sources of error in a measuring system, rather than merely to be aware of the details of their classification. Analysis of the causes of errors is helpful in attaining the necessary knowledge of achieved accuracy. There are many different sources of error that influence the precision of a measuring process in a variety of ways according to the individual situation in which such errors arise. The permutation of error sources and their effects, therefore, is quite considerable. In general, these errors can be classified under three main headings: 1. Process environment 2. Equipment limitation 3. Operator fallibility These factors constitute an interrelated three-element system for the measuring process as shown in Figure 25.2. The requirement of any precision measuring instrument is that it should be able to represent, as accurately as possible, the dimension it measures. This necessitates that the instrument itself have a high degree of inherent accuracy. Small inaccuracies will exist, however, due to the tolerances permitted in the instrument’s manufacture. These inaccuracies will influence the degree of precision attainable in its application. The areas in which operator fallibility arise can be grouped as follows (Rashed and Hamouda 1974): 1. Identification of the measuring situation 2. Analysis of alternative methods



Process environment Operator fallibility

• Temperature • Vibration

• Identification of the situation

• Structural instability • Humidity

• Analysis of alternative methods

• Factors of atmospheric pollution

• Selection of equipment

• Atmospheric pressure

• Application/ measurement

• Gravity

Measured characteristics

Equipment limitation • Sensitivity • Accuracy and precision • Consistency • Repeatability

Figure 25.2 Factors affecting the measuring process.

Part IV.E

3. Selection of equipment 4. Application (or measurement) The identification of measuring situations becomes increasingly complex in modern metrology. As parts become smaller and more precise, greater attention has to be paid to geometric qualities such as roundness, concentricity, straightness, parallelism, and squareness. Deficiencies in these qualities may consume all of the permitted design tolerance, so that a simple dimensional check becomes grossly insufficient. Operators have to be knowledgeable about what they have to measure and how satisfactorily the requirements of the situation will be met by the measuring instrument. Correct identification of the measuring situation will eliminate those methods unsuitable for the situation. Proper measuring equipment can therefore be selected from a smaller range of measuring process alternatives. Method analysis can then be applied to these alternatives to determine which best satisfies the situation. This usually involves examining each method for different characteristics and evaluating the relative accuracies between the different methods.



Accuracy Accuracy is the degree of agreement of individual or average measurements with an accepted reference value or level (American Society for Testing and Materials 1977). Measurement science encompasses two basic approaches for determining conformity to measurement accuracy objectives: (1) an engineering analysis to determine all causes of error, and (2) a statistical evaluation of data after stripping or eliminating the errors revealed by the engineering analysis (Darmody 1967).

Precision Precision is the degree of mutual agreement among individual measurements made under prescribed like conditions, or simply, how well identically performed measurements agree with each other (American Society for Testing and Materials 1977). This concept applies to a process or a set of measurements, not to a single measurement, because in any set of measurements, the individual results will scatter about the mean. Since the means of the results from groups of measurements tend to scatter less about the overall mean than individual results, reference is commonly made to the precision of a single measurement as contrasted with the precision of groups of measurements, but this is a misuse of the term. What is really meant is the precision of a set of single measurements or the precision of a set of groups of measurements (McNish 1967).

Part IV.E

Sensitivity and Readability The terms sensitivity and readability often are used in discussing measurement, and sometimes the concepts they involve are confused with accuracy and precision (see McNish 1967). Sensitivity and readability are primarily associated with equipment, while accuracy and precision are associated with the measuring process. The most sensitive or the most readable equipment may not always lead to the most precise or the most accurate results. Sensitivity can be defined as the least perceptible change in dimension detected by the measuring tip and shown by the indicator. Readability is the ease of reading the instrument scale when a dimension is being measured. It is a factor that should remain constant over the full scale range.

Consistency Consistency is another characteristic of the measuring instrument. Consistency of the reading on the instrument scale when the same dimension is being measured is necessary. This property affects the performance of the measuring instrument and, therefore, complete confidence in the accuracy of the process cannot be established in the absence of consistency.

TRACEABILITY Traceability is a process intended to quantify a laboratory’s measurement uncertainty in relationship to the national standards. It is based on analyses of error



contributions present in each of the measurement transfers: the calibration of the laboratory’s reference standards by NIST, the measurements made in the calibration transfers within the laboratory, and the measurements made on a product. Evidence of traceability is normally required; it may be as simple as retention of certificates and reports on calibration or as complex as reproduction of the analyses demonstrating the uncertainties claimed for the measurements (Rice 1986). A laboratory that maintains its own reference standards (that is, it relies on no laboratory other than NIST for calibration of its standards) must continuously monitor its own performance. Measurements on check standards, intercomparisons of standards, and participation in measurement assurance programs sponsored by NIST are meant to quantify laboratory error sources, as well as to provide indications of the causes (Rice 1986).

Measurement Assurance

Part IV.E

Measurement assurance, thought by some to relate only to methods used in the metrology or calibration laboratory to secure calibrations by NIST, is one of the more important concepts in the measurement field. Traditionally, calibrations by NIST determine the accuracy and precision of the measuring instrument. Measurement assurance protocols (MAPs), on the other hand, are able to include not only the accuracy of the item, but also the contribution to error by the metrologist/technician, laboratory environment, and practices/procedures of the laboratory because the experiment involves measurements by participants in their own laboratories (Belanger 1980). Measurement assurance, in addition to being a concept of importance to metrology and calibration laboratory managers, is one that should interest quality assurance personnel involved in testing and measurement. Most factory testing and measuring involves the use of equipment whose accuracy has been determined through calibration. Little, if any, consideration is given to errors that may be contributed by the test operator, by his or her instructions or procedures, or by the environments in which the equipment is operated. The application of measurement assurance can serve to reduce errors (Rice 1986).

CALIBRATION Calibration refers to measurements where the individual values are reported, rather than to measurements indicating only that an instrument is functioning within prescribed limits. It also refers to the disciplines necessary to control measuring systems to assure their functioning within prescribed accuracy objectives. The general calibration provisions for a measuring system include: 1. Acceptance calibration of a new system 2. Periodic calibration of the system in use or when placed in use after storage 3. Availability of standards traceable to the national standard for the unit of measure under consideration



Normally, a calibration chain or pyramid of echelons is involved in the discipline of metrology control and surveillance. The levels include: Level 1. The product tolerance or measured quantity. Level 2. The calibration of the product measuring system. Level 3. The calibration of the measuring system used to calibrate the product measurement system. Level 4. Local standards, such as gage blocks or standard cells (volts), used for calibration of level 3. Level 5. Referencing local standards of level 4 to the national standard. Each of these levels attempts to achieve an accuracy/tolerance ratio that will satisfy requirements of the preceding level. This achievement is, of course, subject to the limitations of the state of the art, as well as cost–accuracy tradeoffs that may come into play. The aim of all calibration activities is ascertaining that a measuring system will function to assure attainment of its accuracy objectives. Periodic calibration of measuring and test equipment is accepted by most as necessary for measurement accuracy. A little more controversial is the question of determining the basis of the period of recalibration. There are a number of techniques in use to establish calibration intervals initially and to adjust the intervals thereafter. These methods include: 1. The same interval for all equipment in the user’s inventory 2. The same interval for families of instruments (for example, oscilloscopes, gage blocks, and so on)

Part IV.E

3. The same interval for a given manufacturer and model number Adjustments of these initial intervals are then made for the entire inventory, individual families, or manufacturer and model numbers, respectively, based on analyses or history. A study conducted for NIST in connection with a review of government laboratory practices identifies these and other methods (Voft 1980).

Calibration Control System A typical calibration program may involve all or most of the following tasks (Rice 1986): 1. Evaluation of equipment to determine its capability 2. Identification of calibration requirements 3. Selection of standards to perform calibration 4. Selection of methods/procedures to carry out the measurements necessary for the calibration 5. Establishment of the initial interval and the rules for adjusting the interval thereafter



6. Establishment of a recall system to assure that instruments due for calibration are returned 7. Implementation of a labeling system to visually identify the instrument’s due date 8. Use of a quality assurance program to evaluate the calibration system (process, control, audit, corrective action, and so on) Selection of the standards, methods, and procedures to carry out the calibration includes the decision relating to where the calibration will be performed. The recall system must be designed to assure that the calibration organization and the using organization are both aware in advance that an instrument will be due for calibration. Labeling instruments to visually display their calibration due dates is a companion feature to the recall system. Labels indicate (by dates, color codes, or similar symbols) the date the instrument is due for its next calibration. This visual identification may be used by the quality assurance organization to ensure that the instrument is not used beyond its due date. Intervals are established in a variety of ways, as discussed previously. Principal objectives of an interval adjustment program include minimizing the potential for out-of-tolerance instruments in user areas, minimizing the costs of calibration, and assuring the required accuracy of instrumentation.

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Chapter 26 F. Measurement System Analysis

Measurement System Analysis (MSA) Calculate, analyze, and interpret repeatability and reproducibility (Gage R&R) studies, measurement correlation, capability, bias, linearity, etc., including both conventional and control chart methods. (Evaluate)

Part IV.F

Body of Knowledge IV.F

Measurement system analysis consists of qualifying the measurement process, determining the adequacy of the measurement system for use, and identifying and estimating the process error. A measurement system is the entire process for obtaining measurements on some quality characteristic of interest; this process includes standards, personnel, methods of measurement, and so on. In this chapter, definitions as well as the concept of gage repeatability and reproducibility are introduced.

TERMS AND DEFINITIONS Systematic and Random Errors Two important and common types of error in measurement system analysis are systematic error and random error. Systematic errors can be caused by human interference, poor manufacturing methods, and measuring device imperfections, for example. This error remains fairly constant over repeated measurements collected under identical conditions. The error is systematic, which results in values that are consistently above or consistently below the true or reference value of the quality characteristic. Random errors vary arbitrarily over all measurements taken under identical conditions. Even when systematic errors have been identified and accounted for, normal random fluctuations will occur. If only random errors are present in the system, then increasing the number of measurements taken will provide a better estimate of the quality characteristic’s true value. 256


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 26: F. Measurement System Analysis 257

Measurement System Error Measurement system error with respect to measurement system analysis consists of variability that can be attributed to gage bias, stability, linearity, repeatability, and reproducibility. Accuracy of a measurement system is made up of bias, linearity, and stability. Repeatability and reproducibility are the components that describe precision, or measurement variation.

Accuracy Accuracy is a qualitative term defined as the difference between the measurement taken and the actual value of the quality characteristic of interest. The three components of accuracy are bias, linearity, and stability. Bias is defined as the difference between the observed average measurement and a reference value, and is a measure of systematic error in terms of the measurement system: Bias = Observed average – Reference value The observed average measurement can be found by measuring a single part multiple times or selecting several parts at random and measuring each part multiple times. The measurements should be taken under identical conditions.

EXAMPLE 26.1

Table 26.1

Trials

Average Bias

Bias and average estimates for parts of different sizes. Parts

1

2

3

Reference value

2.00

3.80

5.60

1

2.10

3.65

6.21

2

1.88

4.00

5.40

3

1.92

3.88

5.26

4

2.05

3.78

5.98

5

2.01

4.10

4.93

1.992

3.882

5.556

–0.008

0.082

–0.044

Part IV.F

Suppose three parts of different sizes are selected and the diameter of each part is measured. These parts represent the normal range of part sizes for which the measurement system is used. A reference value is known for each of the parts. Suppose each part is measured five times, with the results displayed in Table 26.1. The observed averages are calculated and the resulting bias estimated for each part.



Hypothesis tests can be carried out to test the significance of bias (see AIAG [2002]). If bias is found to be significant, the cause for bias should be identified. Some reasons for significant bias might include (but are not limited to) an incorrect reference value, a worn measuring device, or improper calibration of or incorrect use of the measuring device. Linearity measures how changes in the size of the part being measured will affect measurement system bias over the expected process range. Consider the previous example with three parts of different sizes. Notice that the bias estimates were quite different across the different sizes. There may be evidence of nonlinearity if as the part size increases, the bias changes significantly. Tests can be performed to determine if nonlinearity, if it exists, is significant. Stability is a measure of how well the measurement system performs over time. It provides a measure of the change in bias over time when the same part is measured. Stability differs from linearity in that only one part, whose reference value is known (or assumed to be known), is measured at different points in time. This is to determine if the measurement system has changed over time and after many uses. In general, accuracy provides information about location, or the relationship between the measurement results and reference value of the quality characteristic.

Part IV.F

Precision Precision is defined as the variation encountered when the same part is measured repeatedly using the same measurement system (under the same conditions). The two components of precision are repeatability and reproducibility. Repeatability represents the variability due to the gage or test instrument when used to measure the same part under identical conditions (that is, same operator measuring the same part). Reproducibility, on the other hand, represents the variability due to different operators or setups measuring the same parts using the same measuring device. Reproducibility represents the variability due to the measurement system. Both repeatability and reproducibility will be discussed in more detail in this chapter.

GAGE REPEATABILITY AND REPRODUCIBILITY* Gage repeatability and reproducibility (R&R) studies are used to determine if a measurement system is capable for its intended purpose. If the measurement system variation is small compared to the process variation, then the measurement system is considered capable. In general, the purposes of a gage R&R study are to: • Determine the amount of variability in the collected data that can be attributed to the measurement system in place • Isolate the sources of variability in the measurement system

* In the literature, you will see the terms “gage” and “gauge” used interchangeably. Both spellings are correct and acceptable.



• Determine whether the measurement system is suitable for use in a broader project

Variance Components When conducting a gage R&R study, it is often assumed that the “parts” and the “operators” are selected at random from larger populations. The parts are typically selected at random so that they represent the entire operating range of the process. Since the parts and operators are randomly selected, there is a measure of variability associated with each. There are situations where the parts or operators may be fixed. To illustrate, suppose the operators are really automatic gages and there are only three total for a particular process. If all three automatic gages are used, then we say that the factor “operator” is fixed. Assessing the capability of fixed factors is beyond the scope of this handbook, but additional information and references can be found in Burdick, Borror, and Montgomery (2005). 2 Gage variability is a function of variance components. Let s Repeatability represent 2 the inherent variability in the gage and s Reproducibility represent the variability due to the different operators (or setups, different time periods, and so on) using the same gage. Specifically, we can write the measurement error variability as 2 2 2 2 s Measurement = s Gage = s Reproducibility + s Repeatability error

Furthermore, suppose part-to-part variability is denoted by s P2. Then total variability can be written as a sum of the two variance components: 2 2 s Total = s Gage + s p2

Part IV.F

In a gage R&R study it is important to accurately estimate these variance components and thus adequately estimate repeatability and reproducibility. Two commonly used methods for estimating repeatability and reproducibility are: 1) the tabular method (also known as the range method) and 2) the analysis of variance method. Both methods will be presented and discussed in this section.

The Tabular Method (Range Method) Gage R&R studies were often conducted using a tabular method. This method is based on information that can be obtained from control charts and using the sample ranges to estimate variability (see Chapter 34 for discussion of the sample range and Chapter 37 for discussion of control charts). Estimating Reproducibility. The steps for estimating reproducibility using the tabular method are as follows: 1. Estimate the average measurement for each “operator.” 2. Find the range of these averages (largest average – smallest average); this is called RO (for operator range). 3. Estimate the standard deviation for reproducibility using the relationship



sˆ Reproducibility =

RO . d2

4. Estimate the variance component for reproducibility: 2

⎛R ⎞ 2 sˆ Reproducibility =⎜ O⎟ . ⎝ d2 ⎠ Estimating Repeatability. The steps for estimating repeatability are as follows: 1. Calculate the range for each part (or sample). – 2. Calculate the average range across all samples; this is denoted R. 3. Estimate the standard deviation for repeatability: sˆ Repeatability = sˆ e =

R . d2

4. Estimate the variance component for repeatability: 2

⎛ R⎞ 2 sˆ Repeatability = sˆ e2 = ⎜ ⎟ . ⎝ d2 ⎠ Estimating Part-to-Part Variability. The steps for estimating part-to-part variability are as follows:

Part IV.F

1. Calculate the average measurement for each part (or sample). 2. Find the range of these averages (largest average – smallest average); this is denoted Rp (for part range). 3. Estimate the standard deviation for parts: sˆ p =

Rp d2

.

4. Estimate the variance component for parts: 2

⎛ Rp ⎞ sˆ = ⎜ ⎟ . ⎝ d2 ⎠ 2 p

Complete details of determining values of d2 for each of the above quantities can be found in AIAG (2002) or Barrentine (2003). Additionally, in AIAG (2002) and Barrentine (2003) you will find details of the range method.

The Analysis of Variance Method One of the reported drawbacks to using the tabular method has been the inability to estimate any possible interaction between operators and parts (or samples).



It is often assumed that the operators are well trained and as a result there should be no significant interaction between these two factors. If, however, there is a significant interaction, this effect should be quantified and taken into consideration when providing estimates of repeatability and reproducibility. Using the tabular or range method, it is not possible to estimate the interaction between operator and part. The analysis of variance (ANOVA) method has become a common choice for practitioners conducting gage R&R studies since the computations can be easily carried out using modern statistical software. (See Chapters 35 and 37 for discussion of interactions, factors, and the general analysis of variance method). Before presenting the ANOVA method, some basic assumptions must be discussed.

The Standard Experiment Suppose the response of interest in a gage R&R study can easily be expressed by a random two-factor model (see Chapter 35 for more details on modeling a response, factors, and two-way ANOVA). We are interested in the factors “parts,” “operators,” and possibly the part-by-operator interaction (see Chapter 35 for details of two-factor interactions). A gage R&R study is to be carried out involving p parts, o operators, and r replicates. Suppose y represents the response of interest, and can be modeled as yijk = m + Pi + O j + ( PO )ij + e ijk for i = 1, 2, . . . , p ; j = 1, 2, . . . , o ; k = 1, 2, . . . , r. where yijk is the kth measurement of the ith part by the jth operator.

Pi represents the effect of the ith part; we assume that Pi is a random factor that follows a normal distribution with mean zero and variance s P2.

Part IV.F

m is the overall process mean.

Oj represents the effect of the jth operator; we assume that Oj is a random factor that follows a normal distribution with mean zero and variance s O2 . (PO) ij represents the part-by-operator interaction effect; we assume that (PO) ij is a random factor that follows a normal distribution with 2 mean zero and variance s PO . e ijk represents random error; we assume that e ijk follows a normal distribution with mean zero and variance s e2. 2 The terms s P2, s O2, s PO , and s e2 are our variance components. As discussed previously, gage variability is a function of these variance components. Specifically, we wrote measurement error (gage) variability as 2 2 2 2 s Measurement = s Gage = s Reproducibility + s Repeatability error



But now we will use the variance components for our random factors to determine repeatability, reproducibility, and part variability. Specifically, reproducibility and repeatability variation can be written as 2 2 = s PO + s O2 s Reproducibility

and 2 s Repeatability = s e2 .

Part-to-part variability is given by s P2. The variability of the total observed measurement is given by 2 2 s Total = s Gage + s P2 .

Estimating the Variance Components The variance components will be estimated using the mean squares obtained from an analysis of variance table. To begin, a standard analysis of variance is conducted assuming that the two-factor standard model given earlier is valid. The reader is encouraged to review Chapter 35 for complete discussion of sum of squares, mean square, ANOVA, p-value, and degrees of freedom. The procedure is as follows:

Part IV.F

• Treat the problem as a designed experiment (see Chapter 39 for details on designed experiments). • Conduct an analysis of variance (set up an ANOVA table—see Chapter 35). • Use the mean square values from the ANOVA table to estimate the variance components (see Chapter 35 for discussion of mean square). Some of the quantities from the ANOVA table are given in Table 26.2. Standard statistical software packages will provide these values so it is not necessary to carry out the calculations by hand.

Table 26.2

Necessary quantities for an analysis of variance.

Source

DF

SS

MS

Part

p–1

SSp

MSp

Operator

o–1

SS o

MS o

(p–1)(o–1)

SS op

MS op

op(n–1)

SSE

MSE

opn–1

SST

Part × operator Error (repeatability) Total



The estimates of the variance components given earlier are Operators: sˆ O2 =

MS o − MS op pr

2 = Part × operator: sˆ PO

Parts: sˆ P2 =

MS po − MS E r

MS p − MS op or

Error: sˆ e2 = MS E It is possible that one or more of the variance components could result in a negative value. Some researchers have maintained that if any variance component estimate is negative, it is set equal to zero. Other researchers recommend using different approaches to estimating these quantities so that the estimates are nonnegative (see Montgomery [2009a] for more details). The variance component estimates are then used to estimate reproducibility, repeatability, part-to-part variation, as well as the total variability, using the equations given previously.

EXAMPLE 26.2

Table 26.3

Part IV.F

An experiment was conducted on the thermal performance of a power module for an induction motor starter. The response was thermal performance measured in degrees C per watt. Table 26.3 displays a partial list of data collected for 20 motors by six operators. Each operator measures all parts twice. The original data has been multiplied by 100 for convenience. (The original problem statement for this example came from Houf and Berman [1988].) The specification limits are LSL = 18 and USL = 58. We assume that each motor and the operators have been selected at random from larger populations. The model of interest involves operators, parts, and the operator-by-part interaction.

Typical data for the gage R&R experiment. Operator 1

Operator 2

…

Operator 6

Part

1

2

1

2

…

1

2

1

44

34

43

44

…

46

46

2 . . . . . . 20

21 . . . . . . 29

23 . . . . . . 31

20 . . . . . . 31

22 . . . . . . 30

…

21 . . . . . . 31

21 . . . . . . 29

… … …

Continued



Continued

In this problem, p = 20, o = 6, and r = 2. We will examine results of the gage R&R study using both the tabular method and the ANOVA method. The calculations for the variance components were carried out using Minitab v15 (2007) for both methods.

Tabular Method Results The results of the tabular method are given in Table 26.4. The second column in Table 26.4 provides the estimates for the variance components: 2 • sˆ Repeatability = sˆ e2 = 0 . 5678 2 • sˆ Reproducibility = sˆ O2 = 0 . 2607

• sˆ p2 = 54 . 8697 2 ˆ2 ˆ2 ˆ2 • sˆ Measurement error = s Gage = s Reproducibi l ity + s Repeatability

= 0 . 2607 + 0 . 5678 = 0 . 828 5 2 2 = sˆ Gage = sˆ p2 • sˆ Total

= 0 . 8285 + 54 . 8697 = 55 . 698 2 The last column in Table 26.4 provides the percent of the total variability contributed by each source. For example, the percent contribution for “Repeatability” was found by

Part IV.F

%Contribution =

2 sˆ Repeatability 0 . 5678 × 100 = × 100 = 1 . 02 . 2 sˆ Total 55 . 6982

From Table 26.4, we see that the largest source of variability is differences between parts.

Table 26.4

Gage R&R estimates using the tabular method.

Source

Variance component

% contribution

Total gage R&R

0.8285

1.49

Repeatability

0.5678

1.02

Reproducibility

0.2607

0.47

Part-to-part

54.8697

98.51

Total variation

55.6982

100.00

Continued



Continued

ANOVA Method Before estimating the variance components using the ANOVA method, we can determine if there is a statistically significant difference between parts or between operators, and if there exists a statistically significant interaction between parts and operators. An analysis of variance was carried out, with the results provided in Table 26.5. The variance components can be estimated as follows (although generally it is not necessary to calculate these by hand): Operators: sˆ O2 =

MSo − MSpo pr

2 Part × operator: sˆ PO =

Parts: sˆ P2 =

MSp − MSpo or

=

13 . 580 − 2 . 060 = 0 . 288 20 ( 2 )

MSpo − MSE r =

=

2 . 060 − 0 . 7 33 = 0 . 6635 2

591 . 479 − 2 . 0 6 0 = 49 . 118 6 ( 2)

Error: sˆ e2 = MSE = 0 . 733 The gage R&R estimates are then: 2 2 = sˆ PO + sˆ O2 = 0 . 6635 + 0 . 288 = 0 . 95 1 5 • sˆ Reproducibility 2 • sˆ Repeatability = sˆ e2 = 0 . 733 2 ˆ2 ˆ2 ˆ2 • sˆ Measurement error = s Gage = s Reproducibility + s Repeatability

= 0 . 9515 + 0 . 733 = 1 . 6845

Part IV.F

• sˆ p2 = 49 . 118 2 2 = sˆ Gage + sˆ p2 • sˆ Total

= 1 . 6845 + 49 . 118 = 50 . 803

Table 26.5

ANOVA for the gage R&R example.

Source

DF

SS

MS

F

P

Part

19

11,238.1

591.479

287.126

0.000

Operator

5

67.9

13.580

6.592

0.000

Part × operator

95

195.7

2.060

2.810

0.000

Error (repeatability)

120

88.0

0.733

Total

239

11,589.7

Continued



Continued

Table 26.6

Gage R&R results using the ANOVA method.

Source

Variance component

% contribution

Total gage R&R

1.6850

3.32

Repeatability

0.7333

1.44

Reproducibility

0.9515

1.87

Operator

0.288

0.57

Operator × part

0.6635

1.31

Part-to-part

49.1181

96.86

Total variation

50.8031

100.00

Part IV.F

As with the tabular method, the variance components for the gage R&R study and the percent contribution can be found using a statistical software package such as Minitab. The results are given in Table 26.6. The slight differences between the estimates computed by hand and those provided by the software package for the ANOVA method are strictly due to round-off error. Based on the results in Table 26.6, we see again that most of the total variability is due to differences in the parts. However, the ANOVA results in Table 26.5 show that there appears to be a significant difference between operators as well as a significant interaction between operators and parts (p-values are zero for all practical purposes—see Chapter 35 for discussion of p-values). Since there is a significant interaction between part and operator, there may be evidence that more operator training is necessary.

Comparison of the Results It is important to more fully examine and compare the results that were obtained with these two methods. The variance component estimates for both methods are repeated in Table 26.7. The differences between the two methods are striking. The tabular method uses sample ranges to estimate the variance components while the ANOVA method uses arguably more efficient estimates based on functions of sample variances (see Chapter 35). In addition, using the tabular method the variance component for the operator-by-part interaction could not be estimated. As a result, we obtain very different estimates for reproducibility and therefore total gage R&R. From the tabular method, total gage R&R is found to be 0.8286 while for the ANOVA method it is 1.6850. Since many of these estimates are also used in the calculation of measures such as signal-to-noise ratios (SNR), precision-to-tolerance ratios (PTR), discrimination ratios (DR), and process capability ratios, for example, it is imperative that the variance component estimates be as reliable as possible. The example provided here illustrates that the two methods could lead to different estimates. In turn,



Table 26.7

Variance component estimates for both methods.

Source

Tabular method

ANOVA method

Total gage R&R

0.8286

1.6850

Repeatability

0.5678

0.7333

Reproducibility

0.2607

0.9515

0.2607

0.2882

–

0.6635

Part-to-part

54.8697

49.1181

Total variation

55.6983

50.8031

Operator Operator × part

it is possible that the two methods could lead to very different conclusions about the adequacy of the measurement system. As a simple illustration, one formula for the precision-to-tolerance ratio is PTR =

6sˆ Gage USL − LSL

(another form uses 5.15 in place of 6). Since sˆ Gage is simply the square root of our variance component for total gage variability sˆ Gage, we can calculate the PTR for our example using results from both the tabular method and the ANOVA method. For the tabular method, PTR is 6sˆ Gage USL − LSL

=

6

(

0 . 8286 58 − 18

) = 0 . 137 .

For the ANOVA method, PTR is PTR =

6sˆ Gage USL − LSL

=

6

(

1 . 6850 58 − 18

) = 0 . 195

Part IV.F

PTR =

For details on PTR, SNR, and DR, see AIAG (2002), Wheeler and Lyday (1989), or Montgomery (2009b). Woodall and Borror (2008) provide a discussion of the relationships between these measures as well.

Control Charts in Gage R&R Studies Control charts (presented in Chapter 37) play an integral role in gage R&R stud– ies. Control charts display information about gage capability. Consider the X and R charts for the thermal performance example (displayed in Figure 26.1). – The X chart shows the gage’s ability to distinguish between parts. In a gage R&R – study, it is desirable for the X chart to have many out-of-control points. Each – point on the X chart represents the average of the two measurements taken by



1

2

– X chart by operator 3 4

5

6

Sample mean

50 UCL = 38.47 – X = 36.87

40

LCL = 35.27 30

20

1

2

R chart by operator 3 4

5

6

3 Sample range

UCL = 2.777 2

1

– R = 0.85

0

LCL = 0

Part IV.F

Figure 26.1

– X and R control charts for the thermal performance example.

an operator on a part. Each point on the R chart represents the range between the two measurements taken by an operator on a part. There are a total of 120 samples on each chart. – The upper and lower control limits on the X control chart were determined – using the average range (see Chapter 37). As a result, the X control chart reflects the within-sample variability, which is related only to gage repeatability. Notice – that many of the points on the X control chart plot beyond the control limits (what we would usually consider evidence that our process is out of control). In a gage R&R study, this is actually desirable since it indicates that the gage is capable of discriminating between different parts. If most of the samples on this control chart plotted within the control limits, it would signify that it is difficult for the gage to clearly identify different parts. In this example, there are several points that lie within the control limits. It may be necessary to determine if these are chance occurrences or if they indicate that the gage is having difficulty discriminating between the different parts. It is not clear-cut in this example, so further investigation is most likely needed. The R chart can provide information about special causes of variation. For example, if many of the ranges plot beyond the control limits, this could indicate problems with operator experience, training, or fatigue, which would also result in differences among operators. It is desirable for the points on the R chart to plot



within the control limits. This condition signifies that the operators exhibit consistency in their use of the gage. The R chart for our example has several points outside the control limits. This is not surprising since our analysis of variance indicated that there was a significant interaction between operator and parts. Further investigation is needed.

Issues and Considerations in Gage R&R Studies

Part IV.F

When designing a gage R&R experiment, a number of issues must be considered, such as the number of parts, the number of operators, and the number of replicates to include. There has been considerable debate about these issues. The “standard” experiment often included 10 parts, three operators, and two replicates. However, research has indicated that these recommendations may not be appropriate for many problems. Burdick and Larsen (1997) demonstrate that the lengths of confidence intervals on the variance components in a gage R&R study are significantly shortened when the number of operators is increased (see Chapter 36 for discussion on confidence intervals). They recommend at least five or six operators in a typical gage R&R study. Increasing the number of parts does not affect the confidence intervals as much as increasing the number of operators. However, it has been shown that if the practitioner has to choose between increasing the number of parts or increasing the number of replicates on each part, a greater benefit is obtained by increasing the number of parts. See the review paper by Burdick, et al. (2003) and the references within for further discussion of these issues in gage R&R experiments. There are a number of assumptions made when using either the tabular method or the ANOVA method to carry out a gage R&R study. One such assumption involves replication (replication is defined and discussed in Chapters 34 and 39). In particular, it is assumed that each measurement (replicate) is made independently of one another where a unique setup or preparation of the measuring device is made before the next measurement is taken. Suppose an operator measures a part four times. If the setup of the measuring device is not changed or reset before the next measurement, then the measurements are not true replicates. If the measurements are taken consecutively without resetting the measuring device, then they are a type of repeated measure. The analysis to obtain the estimates of the variance components would have to be different than what has been presented here. Another assumption related to replication is randomization (randomization is discussed more fully in Chapters 34 and 39). Randomization in a gage R&R study is understood to mean that the operator measures each part in random order. A part is selected at random, measured, and then the next part randomly selected and measured. The operator does not randomly select a part, take four measurements, put it back, and then select the next part. In that case, the randomization is a form of restricted randomization and requires estimation of repeatability and reproducibility using methods other than what has been presented previously. There are numerous applications where complete randomization or true replication is not practical or possible. In those situations, other methods would have to be employed to provide reliable estimates of the necessary variance components.



Part IV.F

Tabular Method or ANOVA Method There are advantages and disadvantages to using either the tabular method or the ANOVA method. The tabular method is easy to carry out using ranges to estimate variance components. In addition, interpretation of the results is often intuitive for the practitioner. However, the tabular method is restricted to investigating a measurement system that involves only parts (with one operator) or parts and several operators. It does not lend itself to more complex measurement systems that may involve more than two factors (parts and operators). Furthermore, it does not adequately lend itself to dealing with systems where randomization is restricted or true replication is not possible. In general, as long as you are interested only in parts and possible operators (and not even the interaction between them), then the tabular or range method can be used—again, only if complete randomization can be guaranteed. The ANOVA method can be more computationally intensive than the tabular method, but with modern computer software this is less of an issue. The ANOVA method is more flexible than the tabular method in that it can handle unusual experimental conditions. For example, the analysis of variance method can be used if there are more factors than just parts or operators. Suppose not only parts and operators but location on the part is also a factor to consider. In this case a nested design may be appropriate. The necessary variance components can be easily estimated using analysis of variance for a nested design (see Burdick, Borror, and Montgomery [2005] for details on gage R&R studies for nested designs). However, the tabular method cannot be used for this more complex experimental situation. It should also be noted that just including a third factor (not necessarily nested) and estimating variance components for the factors, all two-factor interactions, and the three-factor interaction is not possible using the tabular approach. A simple extension of the standard two-factor design can not be handled using the tabular approach. In summary, the analysis of variance method for estimating repeatability and reproducibility is more flexible than the tabular or range method. It also uses more efficient estimates than sample ranges to obtain the necessary variance components’ estimates. With modern computational capabilities, the ANOVA method is no more difficult to carry out than the range method. For further details on gage R&R studies or measurement systems in general, please see AIAG (2002), Barrentine (2003), Borror, Montgomery, and Runger (1997), Burdick, Allen, and Larsen (2002), Burdick, Borror, and Montgomery (2003, 2005), Dolezal, Burdick, and Birch (1998), Engel and deVries (1997), Jensen (2002), Larsen (2002), Mader, Prins, and Lampe (1999), Majeske and Andrews (2002), Montgomery (2009), Montgomery and Runger (1993a, b), and Vardeman and VanValkenburg (1999).

Attribute Gage R&R Studies Methods for assessing the capability of a quantitative measurement system as discussed in this chapter are well documented in the literature. When the measurement system involves attribute data, the standard quantitative methods are no longer appropriate. An attribute gage measurement system is appropriate when



the parts or objects of interest are placed into one of two or more possible categories. The measurement of interest is the classification of the part. Several assessment statistics and approaches that deal with categorical measurements include: 1. Appraiser agreement statistics such as kappa statistics and intraclass correlation 2. The analytic method 3. Latent-class models These three approaches can provide some measure of reproducibility or repeatability, and in some cases bias. For more details on appraiser agreement statistics, the reader is encouraged to see Banerjee, Capozzoli, McSweeney, and Sinha (1999), Bloch and Kraemer (1989), Cicchetti and Feinstein (1990), Cohen (1960), Conger (1980), de Mast and van Wieringen (2007), Feinstein and Cicchetti (1990), Fleiss (1971), and AIAG (2002). For more discussion of the analytic method or latent-class models see AIAG (2002), Agresti (1992, 1988), Agresti and Lang (1993), Banerjee, Capozzoli, McSweeney, and Sinha (1999), Boyles (2001), de Mast and van Wieringen (2004), McCaslin and Gruska (1976), Sweet, Tjokrodjojo, and Wijaya (2005), Uebersax and Grove (1990), and van Wieringen and van Heuvel (2005).

Nonmanufacturing Applications of Measurement System Analysis

SUMMARY OF PART IV The material in Part IV covered section IV of the ASQ Body of Knowledge (BoK) for the Certified Quality Engineer Examination (Product and Process Control). It includes the following elements:

Part IV.F

In this chapter, measurement system analysis has been presented for typical manufacturing situations. There are of course numerous applications of agreement analysis in nonmanufacturing settings. Many of the kappa statistics described here originated in the medical statistics and psychometrics fields. As more quality engineers become involved in the service sector, for example, it is imperative that they understand the use of appropriate statistical methods for assessing the capability of the measurement system.

1. Product and process control methods 2. Material control, including material identification, status, and traceability, material segregation, classification of defects, and material review board (MRB) 3. Acceptance sampling, including sampling concepts, sampling standards and plans, and sample integrity 4. Measurement and test, including measurement tools and destructive and nondestructive tests 5. Metrology 6. Measurement system analysis (MSA)



Part IV.F

272 Part IV: Product and Process Control

Control plans are used to document and communicate the plan for monitoring and controlling the process. They summarize information from various sources into a single, handy document for quick reference on the production line. The format of the control plan is not important; standard spreadsheets are acceptable. The control plan should be checked to verify that all critical and significant characteristics identified during the design and process FMEAs are included. Material control is an intrinsic part of quality engineering. The first step in material control always is to mark the item that must be controlled. But marking is of little value without a strong data processing system and accompanying procedures to track the items through the system. Not only individual items but also lots and sublots must be identified and kept separate. With this information in hand it is possible to efficiently conduct product recalls when necessary. Decisions about the disposition of nonconforming material must be made in a careful and well-documented manner. Strict procedures are required to avoid reduction of outgoing quality, as well as unnecessarily wasting nonconforming materials that have some residual value. An appropriate board, such as the material review board, must be established to develop policy and to supervise the subsequent work. Acceptance sampling has been considered one of the most widely used tools of statistical quality control. This section explored different topics in acceptance sampling, including general concepts such as lot-by-lot protection, average quality protection, producer’s and consumer’s risks, operating characteristic (OC) curves, definitions (AQL, LTPD, AOQ, AOQL), standard sampling schemes (ANSI/ASQ Z1.4-2003 and ANSI/ASQ Z1.9-2003) and types of acceptance sampling plans (single, double, multiple, continuous, sequential). The general characteristics and components of the measurement process were presented. Tools and techniques for dimensional and angular measurements, surface roughness, and roundness were covered. An introduction to coordinate measuring machines and their performance also was presented, since these constitute the most advanced metrology equipment available to industry today. The section also covered different nondestructive testing techniques. An attempt has been made to define different concepts in metrology such as calibration, traceability, and measurement errors. Metrology and measuring system terminology also was defined, such as accuracy, precision, sensitivity, and so on. A brief introduction to dimensional and geometric tolerancing also was made. Finally measurement system analysis (MSA) was presented, emphasizing gage repeatability and reproducibility (gage R&R) studies.



Part V Continuous Improvement Chapter 27 Chapter 28 Chapter 29 Chapter 30 Chapter 31

A. Quality Control Tools B. Quality Management and Planning Tools C. Continuous Improvement Techniques D. Corrective Action E. Preventive Action

Some of the most successful organizations are those in which all members believe that a part of each person’s daily job is the improvement of the processes they work with. Part V describes tools and techniques for accomplishing these vital tasks. It is divided into five chapters: Quality Control Tools, which describes the seven original problem-solving tools; Quality Management and Planning Tools, which discusses what have become known as the seven new tools; Continuous Improvement Techniques, which provides an introduction to several of the broader, more systematic approaches to quality; Corrective Action; and Preventive Action.

Part V

273



Chapter 27 A. Quality Control Tools

Select, construct, apply, and interpret tools such as 1) flowcharts, 2) Pareto charts, 3) cause and effect diagrams, 4) control charts, 5) check sheets, 6) scatter diagrams, and 7) histograms. (Analyze) Body of Knowledge V.A

Quality control tools as defined by the American Society for Quality (ASQ) and as accepted throughout the quality engineering community include: • Flowcharts • Pareto charts • Cause-and-effect diagrams • Control charts • Check sheets • Scatter diagrams

Part V.A

• Histograms Collectively, these tools are commonly referred to as the seven basic tools. Kaoru Ishikawa (1985) is credited with making the following statement with respect to these tools: “. . . as much as 95 percent of all quality-related problems in the factory can be solved with seven fundamental quantitative tools.” Ishikawa’s statement provides three key insights into these tools, namely that these seven tools are: 1. Applicable in problem-solving situations most commonly encountered by CQEs 2. Quantitative in nature and rely, with possibly the exception of flowcharts and cause-and-effect diagrams, on numerical data

274


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 27: A. Quality Control Tools 275

3. Most commonly used in quality control—that is, as aids in tracking, monitoring, and analyzing data—as opposed to the planning functions associated with quality assurance This chapter discusses six of the seven basic tools (control charts are discussed in considerable detail in Part VI). The next sections discuss six of the tools, the graphical tools, describing the purpose for each tool, information about the tool’s applications and mechanics, and at least one illustration of the tool’s use.

FLOWCHART The purpose of a flowchart is to provide a graphic representation of the elements, components, or tasks associated with a process.

Applications Flowcharts are helpful for documentation purposes and, through standardized symbols, promote a common understanding of process steps and the relationships/ dependencies among those process steps. Flowcharts can be prepared for and used at a high level, where readers/users of the flowcharts may not be familiar with process-specific jargon or terminology. In the high-level application, flowcharts are intended to help readers/users understand what may be a complex process without providing unnecessary, and potentially confusing, detail. Likewise, flowcharts can be prepared for and used at a detail level where readers/users have familiarity and expertise with a given process. In the detaillevel application, flowcharts are intended to help readers/users perform analyses most commonly related to optimization or process improvement.

Mechanics

2. List major steps/tasks and decision points. List, in sequential order, each of the major steps or tasks and decision points that occur as part of the process between the start and stop points.

Part V.A

1. Select a start and stop point. A flowchart, by definition, must specify start and end points. Since it is possible to have many flowcharts describing various sections, elements, or components of a process, particularly when the process gets large and complex, start and end points for flowcharts are defined in terms of boundaries. Boundaries are naturally occurring breaks or division points that separate processes or systems at the macro level or sections, elements, or components of a process at the micro level.

3. Use standardized graphical symbols to document the process. Using standardized symbols, document each of the steps/tasks identified above. Placement of appropriately labeled symbols and use of arrows


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 276 Part V: Continuous Improvement

Start/stop symbol

Flow line symbol

The general symbol used to indicate the beginning and end of a process is an oval. A line with an arrowhead is the symbol that shows the direction of the stages in a process. The flow line connects the elements of the system.

Basic processing symbol The general symbol used to depict a processing operation is a rectangle.

Decision symbol

Figure 27.1

A diamond is the symbol that denotes a decision point in the process. This includes attribute-type decisions such as pass–fail, yes–no. It also includes variable-type decisions such as which of several categories a process measurement falls into.

Four primary flowcharting symbols.

defines the sequence of events. Four primary flowcharting symbols are depicted in Figure 27.1. While there are many symbols for flowcharting, these primary flowcharting symbols are capable of and adequate for documenting any process. 4. Review results. Compare the flowchart with the process to verify that the flowchart is complete and accurately describes the process. Having more than one person independently verify the flowchart is generally considered standard protocol.

Illustration

Part V.A

Hallock, Alper, and Karsh (2006) present a process improvement study on diagnostic testing in an outpatient healthcare facility. The purpose of the study was to determine what factors contributed to the delay of notification of test results to patients. A general flowchart for overall diagnostic testing process was presented similar to the one in Figure 27.2.

PARETO CHART The purpose of a Pareto chart is to identify those “vital few” areas that account for the largest frequency or relative frequency in a data set and separate those vital few areas from the “trivial many.”

Applications A Pareto chart graphically depicts the “80/20 rule” originally postulated to explain economic phenomena by the Italian economist Vilfredo Pareto and later adapted for quality applications by Juran and Gryna (1980). The 80/20 rule allows



Prep for visit

No

Patient examined by doctor

Tests ordered

Patient discharged

Yes Tests are ordered

Tests run/ samples collected

Sample delivered for testing

Test results delivered to doctor(s)

Results interpreted

Patient notified

Figure 27.2

End

Flowchart for diagnostic testing process.

readers/users to identify and focus on the approximately 20 percent of factors (that is, columns or categories) that account for approximately 80 percent of potential problems.

Mechanics

2. Prepare the graphic. As the data are rearranged for display from a check sheet or histogram to a Pareto chart, the title of the chart changes, as do the column or category titles when the corresponding data are placed into different column or category locations.

Part V.A

1. Rank order the columns or categories of data. In a Pareto chart, columns or categories of data displayed previously as check sheets or histograms are rank ordered from the highest frequency or relative frequency on the left to the lowest frequency or relative frequency on the right.

3. Calculate and place on the graphic a relative frequency line above the data columns or categories. A relative frequency line can be calculated and placed above the data in a Pareto chart for quick assessment of the relative contribution made by each column or category.



Frequency

Pareto chart

D

A C B Category

E

Figure 27.3 Typical Pareto chart. Source: W. Stevenson, “Supercharging Your Pareto Analysis,” Quality Progress (October 2000): 51–55. Used with permission.

Illustration Figure 27.3 depicts a Pareto chart related to the specific types of problems found in the inspection of school buses (see Stevenson 2000). Each column for the tally data corresponds to an occurrence of a problem, and the columns or categories have been rank ordered as follows for the Pareto chart: • Column D: Worn seats • Column A: Dirty floors • Column C: Exterior scratches • Column B: Cracked windows • Column E: Faulty brakes

Part V.A

CAUSE-AND-EFFECT DIAGRAMS The purpose of a cause-and-effect diagram, also known as a fishbone diagram or Ishikawa diagram, is to graphically document the analysis of factors (that is, causes) that relate to a single problem or opportunity (that is, effect).

Applications Cause-and-effect diagrams are used in problem-solving situations and in general analysis to identify factors (that is, causes) related to a problem or opportunity (that is, effect) to help the problem-solving or analysis team understand how those factors may cause the given effect, and to help the problem-solving or analysis team focus on “next steps” in process improvement.



Mechanics 1. Select a single problem or opportunity (that is, effect). A cause-and-effect diagram is useful for analyzing only one problem or opportunity. The problem or opportunity that is selected for analysis is documented by a keyword description or short narrative description placed in a rectangle or box, generally on the right side of the diagram. When analyzing more than one problem or opportunity, a different causeand-effect diagram is used for each problem or opportunity. 2. Identify the major causes of the problem or opportunity. Cause-and-effect diagrams have been adequately described as fishbone diagrams where major causes are documented as the major bones of a fish skeleton. Major causes are generally described as they relate to people, hardware/equipment, the intended operating environment, methods, and materials. Teams should be formed to brainstorm possible causes or opportunities. 3. Identify the minor causes associated with each major cause. For each major cause (that is, people, hardware/equipment, environment, methods, and materials) associated with a problem or opportunity, minor causes are identified. Identification of minor causes may be graphically described as adding more structure to the fishbone skeleton. Minor causes appear graphically as “bones” attached to a major cause. 4. Identify additional cause structure. The analysis continues, adding detail to the fishbone structure until all causes associated with a problem or opportunity have been identified and documented. The analysis may continue until several more layers of detail have been considered and added to the diagram.

Illustration Figure 27.4 depicts a high-level cause-and-effect diagram before detailed analysis is started. As mentioned previously, a single problem or opportunity is identified on the right side of the graphic. Major causes are normally associated with one or more of the following: • People (personnel)

• Environment • Methods

Part V.A

• Hardware/equipment

• Materials Major causes graphically represent the major bones of a fish while minor causes represent additional structure in the diagram. Figure 27.4 generally is the starting point for a cause-and-effect analysis and, therefore, may be used as a template to help QEs begin.



(Cause) Hardware/ equipment

(Cause) Environment

(Cause) People

Effect

Methods (Cause)

Figure 27.4

Materials (Cause)

Cause-and-effect diagram/template.

(Cause) Hardware/ equipment

(Cause) Environment Weather

(Cause) People

Riders Abusive treatment

Driving conditions

Drivers

Inadequate training Maintenance personnel

Drivers Time of day/night

Riders

Dirt Road surfaces Pavement

Bus safety discrepancies Load/unload Riding

Original equipment/ parts

Driving

Repair/replacement equipment/parts Maintenance/ repair

Part V.A

Methods (Cause)

Figure 27.5

Materials (Cause)

Cause-and-effect diagram: bus safety discrepancies.

Figure 27.5 illustrates a continuation of the example shown in Figure 27.4 originally conceived by Stevenson (2000). In Figure 27.5 we see that the effect of interest is “bus safety discrepancies.” We also see that the major causes of people, hardware/equipment, the intended operating environment, methods, and materials have been identified. Associated with each major cause are a series of supporting causes related to the major cause. In the major cause “environment,” for example, we see that “driving conditions” is a causal factor associated with bus safety discrepancies.



Continuing with the analysis of driving conditions in the major cause of environment, we see that weather (that is, rain, snow, sleet, fog, and so on), as well as the time of day or night, can cause the effect. Road surfaces (that is, dirt or paved) also cause the effect. The analysis continues until each major cause has been investigated and enough supporting structure has been added to the diagram to identify all the causes associated with the problem or opportunity.

CHECK SHEETS The purpose of a check sheet is to summarize, and in some cases graphically depict, a tally count of event occurrences.

Applications A check sheet is used when readers/users are interested in counting the number of occurrences of an event, such as defects. In many instances, a check sheet will summarize count data related to certain types of defects and will provide a rough graphical representation of where in a part or process defects occur.

Mechanics 1. Design the check sheet for a given application. A check sheet is a tool designed for a specific application and must, therefore, include any and all information pertinent to the application. In general, the design of a check sheet should include enough administrative data to facilitate referencing and analysis. Administrative data frequently include identification of the product or process, duration of the data collection period, individuals responsible for the product or process, and individuals responsible for the data collection. A check sheet should also include space to record tally data for event occurrences, a rough graphical representation of where in the part, product, or process events occur, and a space to record remarks.

3. Use the data for analysis or input to additional graphical tools. Count data summarized on a check sheet frequently are analyzed to identify, track, or monitor defects associated with a particular area on a part or location in a process. The analysis performed on check sheet data frequently is used to trigger process improvement efforts or the data are used as input to other graphical tools, such as histograms and Pareto charts.

Part V.A

2. Record the data. Using the space provided to record tally data, indicate each occurrence of an event with a symbol such as an “x,” check mark, circle/dot, and so on. Each event occurrence receives one mark or symbol. Check sheets also frequently identify, through a rough graphical representation, where in the part or process events occur by highlighting that portion of the rough graphical representation provided.



Frequency

Check sheet

A

Figure 27.6

B C D Category

E

A simple check sheet.

Source: W. Stevenson, “Supercharging Your Pareto Analysis,” Quality Progress (October 2000): 51–55. Used with permission.

Illustration Figure 27.6 depicts tally data related to specific types of problems found during the inspection of school buses (Stevenson 2000). Each column for the tally data corresponds to an occurrence of a problem, as follows: • Column A: Dirty floors • Column B: Cracked windows • Column C: Exterior scratches • Column D: Worn seats • Column E: Faulty brakes

Part V.A

There are many different types of check sheets that can be created. The user should customize the check sheet by including such information as dates, shifts, and so on, to allow for ease of interpretation. See Bothe (2001) or Montgomery (2009b) for more details.

HISTOGRAMS The purpose of a histogram is to graphically depict the frequency of occurrence of events, where event occurrences are sorted into categories of a defined range along a continuous scale.

Applications Histograms are helpful for displaying the distribution of event occurrences among the various columns or categories of event types. Histograms are used when it is important to see and understand how a particular set of data are distributed



relative to each other, and possibly relative to a target or tolerance. The data are recorded in each column or category as they occur, and columns are not sorted by frequency.

Mechanics 1. Determine the amount of data to be collected. As a starting point for a histogram, it is necessary to identify approximately how much data will be collected. One data point will be collected for each event occurrence. 2. Determine the number of columns or bins to be used. There are many different guidelines available for determining the number of bins. For example, one recommendation is that the number of bins be approximately equal to √–n–, where n is the number of data points. Computer software packages use several different algorithms for determining the number of bins, including those based on Scott (1979), Freedman and Diaconis (1981), and variations of Sturges’s rule (Sturges 1926). 3. Collect and record data. As data for a histogram are collected, they are recorded in tabular or tally form. 4. Prepare the graphic. To prepare the histogram for plotting data, it is necessary to provide a descriptive title for the graphic, label each axis, provide a measurement scale for each axis, label the columns, and provide a data summary. 5. Graph the data. Using the data summary, plot the frequency or relative frequency in each column. It should also be noted that the histogram is often considered a large-sample graphical technique and can be unreliable for small sample sizes. Some researchers argue that the histogram should not be used for samples with less than 50 to 75 observations. For small samples, the histogram can be quite sensitive regarding the number and width of the bins chosen.

Illustration

Part V.A

With the increase in the use of high-strength concrete mixtures in roadway and bridge construction, quality improvement and quality assurance procedures have become an important aspect of production monitoring. Reducing the use of costly but necessary materials while maintaining a high level of quality and meeting required specifications has become increasingly important due in part to the growing demand for materials worldwide. Quality improvement tools will aid suppliers in improving the manufacturing process and reducing product variation and unnecessary waste. One important quality characteristic is the compressive strength of concrete, which is directly related to the amount of Portland cement used (there are many other variables influencing compressive strength). Figure 27.7 displays a histogram of compressive strengths for 133 samples collected for



3500

Relative frequency (percent)

20

15

10

5

0 3500

4000

4500

5000

5500

6000

Compressive strength (psi)

Figure 27.7

Histogram of compressive strength of concrete samples.

a particular product from one company. The minimum acceptable compressive strength in this case is 3500 psi, which is indicated on the histogram in Figure 27.7. The histogram clearly shows that quite often the strength of concrete delivered can be as much as 1500 psi to 2500 psi higher than the specified minimum. The amount of cement that could be saved by reducing the total cement content in the mixture is significant.

SCATTER DIAGRAMS The purpose of a scatter diagram is to graphically display indications of a relationship between two variables.

Part V.A

Applications A scatter diagram is used in the analysis of quantitative data where a QE may be interested in how a variable may perform or behave relative to another variable. The relationship being investigated is called a correlation, and Figure 27.8 identifies three possible relationships as positive correlation, no correlation, and negative correlation. Correlation is discussed in detail in Chapter 36.

Mechanics 1. Select two variables of interest. The scatter diagram focuses on possible correlations between two variables. The two variables of interest should have the potential for a cause-and-effect relationship.



y

1. Positive correlation. An increase in y may depend on an increase in x.

0

x

y

2. No correlation. There is no demonstrated connection between x and y.

0

x

y

3. Negative correlation. A decrease in y may depend on an increase in x.

0

Figure 27.8

x

Three possible relationships identified by scatter diagrams.

2. Set a scale for the axes. Since one variable will be plotted on the x-axis while the other variable is plotted on the y-axis, a scale must be selected for each axis such that the data use all, or nearly all, of the scale. 3. Collect and chart the data. Having set up the graphic, collect and chart or plot the data in accordance with the scale specified. 4. Evaluate the results. Using Figure 27.8, evaluate the results to identify any relationships.

Illustration

Part V.A

Table 27.1 provides the data for Figure 27.9. The data provided are derived from a training analysis involving the number of hours spent in training as compared to the number of defects produced by employees who received varying amounts of training. The x-axis (representing training hours) documents how many hours employees spent in training. The y-axis (representing defects) documents tally or count data of the number of defects produced by employees who received the training.

RUN CHARTS While run charts are not specifically identified in the QE BoK, run charts are becoming an increasingly important tool for QEs.



Table 27.1

Training data. Training Hours versus Number of Defects

Observation

Training hours

Defects

Observation

Training hours

Defects

1

1.00

33

10

3.25

23

2

1.25

33

11

3.50

20

3

1.50

32

12

3.75

17

4

1.75

31

13

4.00

14

5

2.00

30

14

4.25

12

6

2.25

28

15

4.50

9

7

2.50

27

16

4.75

8

8

2.75

27

17

5.00

8

9

3.00

25

18

5.25

7

35 30

Defects

25 20 15 10 5 0 0.00

1.00

2.00

3.00

4.00

5.00

6.00

Part V.A

Training time, hours

Figure 27.9

Training time versus defects.

The purpose of a run chart is to track and monitor the number of event occurrences over time.

Applications A run chart is used to help QEs understand how a parameter or metric is behaving or performing over time. The run chart tracks and monitors a metric or parameter



without regard to control limits or tolerances. In fact, it is the exclusion of control limits or tolerances that differentiates the run chart from various types of control charts.

Mechanics 1. Select a parameter or metric of interest. The run chart focuses on only one parameter or metric. 2. Set a scale for the y-axis. Once the parameter or metric has been selected, it will be graphed on the y-axis or vertical axis. A scale must, therefore, be set for the y-axis in such a manner that distributes the data throughout the scale. 3. Identify the time intervals for the graphic. Since the run chart displays data over time, the time frame must be meaningful for the application. Time frames such as hourly, each shift, daily, weekly, and monthly are commonly used. 4. Collect and chart the data. Having set up the graphic, collect and chart or plot the parameter or metric over the time intervals specified. 5. Calculate the average. The parameter or metric average is normally calculated for a run chart once sufficient data have been collected. A line indicating the average is plotted directly on the run chart.

Illustration Figure 27.10 continues with an extension of the data originally introduced in Figure 27.6, considering the case of defects associated with a school bus as originally conceived by Stevenson (2000). Stevenson originally discussed a set of data identifying 27 defects or deficiencies associated with a school bus safety inspection. It is reasonable to extend 45 38 35

35

32

30 25

28 23

27

20

Average = 22.67 21

15

25

17 13

Part V.A

Number of discrepancies

40

8

10

5

5 0

Sept Oct Nov Dec Jan Feb Mar Apr May June July Aug

Figure 27.10

Run chart.



Stevenson’s analysis by concluding that the inspection occurred at a specific time—say in September at the start of the school year. Figure 27.10 shows that in September there were 27 defects or deficiencies recorded during a regular inspection of the school bus fleet. Since safety inspections are a regularly occurring event, it would be reasonable, interesting, and important to track and monitor the results of similar inspections as they occur monthly during the course of a school year. The results of such inspections are summarized in Figure 27.10. Figure 27.10 indicates that safety inspections were completed each calendar month. As would be expected, the number of defects or discrepancies is higher in the beginning of the school year when the buses are used very frequently and decreases substantially later in the year when the buses are not used as frequently. The parameter of metric average in this case is 22.67 defects or discrepancies per month.

CONTROL CHARTS As was mentioned earlier in this chapter, control charts will be thoroughly discussed in Chapter 37 of this book.

SUMMARY OF THE GRAPHICAL TOOLS

Part V.A

The tools discussed in this section, while helpful for quality assurance or planning, are primarily intended for quality control. In the quality control phase of production or service delivery, process optimization and improvement take over from design and development. As the transition is made from design and development to optimization and improvement, a new set of tools is needed.



Chapter 28 B. Quality Management and Planning Tools

Select, construct, apply, and interpret tools such as 1) affinity diagrams, 2) tree diagrams, 3) process decision program charts (PDPC), 4) matrix diagrams, 5) interrelationship digraphs, 6) prioritization matrices, and 7) activity network diagrams. (Analyze) Body of Knowledge V.B

The concept of quality has existed as long as people have existed. Qualities, defined as physical or nonphysical characteristics that constitute the basic nature of things, are readily accepted as part of the package that encompasses a good or a service. Shewhart (1980) captured the concept in the first part of the 20th century: There are two common aspects of quality, one of these has to do with the consideration of the quality of a thing as an objective reality independent of the existence of man. The other has to do with what we think, feel, or sense as a result of the objective reality—this subjective side of quality is closely linked to value.

Part V.B

Shewhart and others such as Deming (1986), Juran (1989), Crosby (1979), Feigenbaum (1983), Ishikawa (1985), Shingo (1986), and Taguchi (1986) have helped us understand the essence of quality and helped us bring it to the point of actionable issues. There have been, and continue to be, a number of approaches and initiatives that advocate quality as a scientific discipline. But, on the other hand, there are also many anecdotal approaches and initiatives that treat quality as an art. Interest in tactical, in-process approaches that stress the importance of meeting substitute quality characteristics (as opposed to strategic approaches that stress true quality characteristics) helped to move the quality concept upstream from final product inspection. This evolutionary branch was eventually called kaizen or incremental improvement (a management-by-fact-related approach) and applied primarily in production-related processes (Imai 1986). Here, evolving practices were observed and eventually tools were identified, described, and adopted. Tools such as the seven basic tools—cause-and-effect diagram, flowcharts, check sheet, histogram, scatter diagram, Pareto analysis, and control charts—were recognized as useful. 289



The Japanese further expanded the quality concept in a formal sense in the late 1970s and early 1980s with what they termed the seven “new” quality tools (Mizuno 1988). This new era was based on two fundamental requirements: (1) the creation of added value over and above consumer needs, and (2) the prevention, rather than the rectification, of failure in meeting customer needs. Hence, these tools were positioned to address strategic (as opposed to tactical) quality issues. Seven tools—relations diagram, affinity diagram, systematic diagram, matrix diagram, matrix data analysis, process decision program chart (PDPC), and arrow diagram—were the result of this initiative. In the 1990s, based on Shewhart’s definition of quality, field experience/ observation, and Ishikawa’s (1985) concepts of true and substitute quality characteristics, Kolarik (1995) postulated a scientific framework. This framework has two major components—the experience of quality and the creation of quality: The experience of quality is a function of the fulfillment of human needs and expectations. We create quality through processes that we develop and maintain (Kolarik 1995). The following pages describe the CQE BoK management and planning tools plus several other useful tools that help us to create quality. These pages describe, position, and illustrate a selected cross section of ten quality-related tools. The seven BoK tools are marked with an asterisk (*). The three remaining tools— process maps, process value chain diagrams, and benchmarking—are included to provide extended quality management/planning capabilities. Affinity diagrams* Interrelationship diagraphs* Tree diagrams* Process decision program charts* Matrix diagrams* Prioritization matrices* Activity network diagrams* Process maps

Part V.B

Process value chain diagrams Benchmarking These tools help to formulate and organize thoughts and ideas so that they can be leveraged directly toward quality/business improvement. More elaborate discussions of quality strategies, initiatives, and tools appear in Kolarik (1999).

AFFINITY DIAGRAMS The purpose of an affinity diagram is to help people collect, organize, summarize, and communicate facts, opinions, and ideas.


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 28: B. Quality Management and Planning Tools 291

Applications The affinity diagram is useful when we are faced with describing, organizing, and communicating the general nature of a relatively complicated situation that can be described in terms of a large number of facts, opinions, and/or ideas. It allows us to group or cluster the facts, opinions, and/or ideas into categories with some common feature so that we can locate/classify/describe/summarize the basic issues. The affinity principle (of association and clustering) is useful in the initial stages of constructing a relationship diagram or in any situation where we desire to discover, summarize, and organize a variety of facts, opinions, and/or ideas.

Mechanics 1. Identify a general theme. The theme may be associated with a problem situation or an opportunity situation, or simply a situation in our physical and/or social environments. 2. Collect facts, opinions, and ideas. Data/information may be generated by a group of people in any number of formats. For example, we can use work teams, focus groups, groups of experts, or data/information existing in files or archives. 3. Express and enter the data/information in a common format. Here, we might use sticky notes on a wall, cards on a table, or computer software capable of expressing each piece of data/information in a medium that can be “moved around.” 4. Identify the groups/clusters. Here, we identify/label/describe the groups or clusters regarding the common attribute(s) or summary characteristics that apply. 5. Cluster the data/information pieces. At this point we cluster or organize our data/information into cohesive groups. 6. Repeat steps 4 and 5 to form supergroups/clusters. It may be possible to relate two or more of the initial groups/clusters and develop a supergroup or supercluster. Supergrouping can be repeated until the facts, opinions, or ideas are suitably classified/organized.

Illustration

Part V.B

7. Present the results. The final product is an organized set of facts, opinions, and ideas that make sense in terms of providing help in understanding the nature of the situation or theme from step 1.

Figure 28.1 depicts the results of a student focus group session. The goal of the focus group was to communicate issues that were important to undergraduate students in their college program. Here we can see that a number of concerns were voiced, in no particular order, and that we have used the affinity principle to sort, organize, and isolate/label relevant issues for further action. For more



Theme: Undergraduate program improvement issues Source of comments: Undergradute focus group

Students need software that works (info software)

Programmatic Sr. design requires advanced level knowledge from last semester classes Students want more electives and choices

Computer facilities and support issue

Curriculum design issue

Students want more computer languages

Interaction

Courses and feedback

Students are sensitive to harsh criticism

Students do not get continuous feedback from courses

Students feel intimidated by some instructors Some instructors are difficult to approach Some instructors treat students as if they were stupid

Computing support

Students need computing support at all hours (lack of computers in dept) Students need unlimited printing privileges Students don't like to hunt down which lab contains which software

Students want to know how they are doing in class Students want level point loading in a course throughout the semester

Student–faculty communication issue

Recognition Students are not recognized for good work

Figure 28.1

Instructor availability Instructors are out of town often Lack of availability of instructors/TA TA/faculty hours/schedules create problems for interaction

Flexibility in office hours/coordinated with TA hours issue Involvement Freshmen and sophomores want to get to know upper class persons Students want to be informed of activities and events Students want to interact with other students Students want to get involved with organizations

Early student awareness issue

Pre-professional practice Students are not aware of the value of internships/co-ops Students have difficulty understanding what is expected in professional practice

Student involvement issue

Student focus group affinity diagram.

information on affinity diagrams see Mizuno (1988), Brassard (1989), and Kolarik (1995, 1999).

INTERRELATIONSHIP DIGRAPHS

Part V.B

The purpose of an interrelationship digraph is to help people discover, visualize, and communicate high-level sequential and/or cause-and-effect relationships.

Applications Constructing an interrelationship digraph is best addressed in a team environment, so as to capture a diversity of perspectives regarding sequences, effects, and causes. Typical starting points include effects or symptoms, both undesirable as well as desirable. Logical development from these effects back to potential causes is common to most relations diagramming efforts. Clustering and sequencing of causes are common to all interrelations digraphs. Boxes, circles, ovals, loops, and directional arrows are used to depict cause-to-effect flows. In general, the interrelationship digraph helps us identify and isolate relevant causal factors concerning a situation—problem or opportunity. Ultimately it helps



us understand and communicate the essence of causal or sequential relationships regarding a situation in our physical and/or social environments. It is a graphical aid in basic problem–opportunity/cause–effect discovery and relationship determination and expression, which helps us to identify and relate basic causal factors, express basic causal sequences, introduce assertions and assess or project resulting effects, and communicate critical relationships. The interrelationship digraph is one form of relationship diagram—see Brassard (1989) for details.

Mechanics The mechanics of constructing a interrelationship diagraph generally follow the same lines as in the affinity diagram, but extend the affinity diagramming process into cause–effect and/or sequential ordering, generally indicated by arrows that connect the “boxes” or statements. 1. Identify a general situation. The situation may be associated with a problem or an opportunity in our physical, economic, and/or social environments. 2. Collect facts, opinions, and ideas. This data/information may be generated from a group of people in any number of formats. For example, we can use work teams, focus groups, or groups of experts. 3. Express and enter the data/information in a common format. Here, we might use sticky notes on a wall, cards on a table, or computer software capable of expressing each piece of data/information in a medium that can be “moved around.” 4. Identify the groups/clusters. Here, we identify/label/describe the groups or clusters regarding the common attribute(s) or summary characteristics that apply and describe their relationship (as a group) to the situation at hand. 5. Cluster the data/information pieces. At this point, we cluster or organize our data/information into cohesive groups. 6. Identify relations/sequences. Once we have basic descriptions and clusters/groups, we express the relationships between these entities with arrows.

Part V.B

7. Repeat steps 4, 5, and 6 to form supergroups/clusters. It may be possible to relate two or more of the initial groups/clusters and develop a supergroup or supercluster. Supergrouping can be repeated until the facts, opinions, or ideas are suitably classified/organized. The result here is a supergroup and its description/relationship to the situation. 8. Present the results. The final product is an organized set of facts, opinions, and ideas that make sense in terms of providing help in understanding the nature of the situation from step 1, and summarizing the situation in a problem or opportunity format that flows logically from the facts and figures.



Some vendors furnish raw materials to High Lift Some vendors furnish finished assemblies to High Lift Some vendors furnish generic parts/supplies to High Lift

Vendors are not aware of line shortages at High Lift

High Lift is a small account to some vendors

High Lift is a large account to some vendors

High Lift uses centralized storage facilities Everything coming through receiving goes to storage and back out to the line eventually

About 35% of late deliveries to the line were also late deliveries to receiving

High Lift does not certify vendors

Hot/shortage items go directly to the line from receiving

Figure 28.2

About 53% of inbound deliveries to receiving are delivered on time

About 80% of line shortages were also late deliveries from vendors

Line shortages lead to lost schedule time and additional costs

New line flow is faster than expected by master scheduling

All received items are palletized for storage

All palletized items are shrink-wrapped before storage

About 69% of line orders are delivered on or before scheduled date

Some vendors have certificates with other customers

Obsolete/damaged/deteriorated materials/parts are purged from storage semiannually—the total runs about 5% of total purchases

About 10% of "on-time" orders to the line are actually late

Line support subprocess interrelationship digraph.

Reproduced with permission, from W. J. Kolarik, Creating Quality: Process Design for Results (New York: McGraw-Hill, 1999): 450.

Illustration The illustration in Figure 28.2 provides a relatively simple interrelationship diagraph where the situational descriptions are grouped and labeled using the affinity principle, then arrows are used to indicate convergence toward a logical, actionable conclusion. For more information on interrelationship diagraphs, see Mizuno (1988), Brassard (1989), and Kolarik (1995, 1999).

Part V.B

TREE DIAGRAMS The purpose of a tree diagram is to help people discover, visualize, and communicate logical hierarchical relationships between critical events or goals/objectives and means.

Applications Tree diagrams are useful in situations where we want to discover or define a hierarchical relationship between events—desirable or undesirable. A fault tree (FT) can be constructed to relate an undesirable “top event” or failure to a sequence of events that led to the top event. In other words, the FT depicts logical pathways



from sets of basic causal events to a single undesirable result or top event. We typically use logical operators, such as AND or OR gates, to connect lower-level events with higher events. Hence, once the logic has been described, quantification can take place and risk level can be assessed.

Mechanics Several steps are involved in the development of the FT: 1. Identify the top event. The top event is an undesirable event that we are motivated to prevent. 2. Identify the next-level events. The second-level events represent events that could lead to the top event. 3. Develop logical relationships between the top and next-level events. Here we use logic gates, for example, AND or OR gates, to connect the second-level events to the top event. 4. Identify and link lower-level events. Now, we develop the logic tree down to the lowest level desired by repeating steps 2 and 3, moving down through event sequences one level at a time. 5. Quantify the FT (optional). Here we develop probability of occurrence estimates for the events in the FT, and then develop a probability statement and estimate for the top event.

Illustration Figure 28.3 presents an FT focused on unintended line shutdowns. This illustration contains OR gates that connect lower-level events with higher-level events. A fault tree does not contain all possible failure modes or all possible fault events that could cause system failure. However, an FT is capable of considering/ modeling human error, hardware and software failures, and acts of nature. It finds widespread usage in the fields of reliability, safety, and risk analysis. The FT is a more focused tool than the failure mode and effects analysis (FMEA). FMEA is sometimes used to help determine the top event in an FT. FT works well for independent events—common cause is difficult to model, especially in terms of quantification.

Other tree diagram formats include event trees, systematic diagrams, and goal trees, as well as concept fans. Event trees are simply tree diagrams that start with an event and work backward from the event by defining binomial response (yes or no) branches. The response branches form a hierarchy of responses that eventually lead to an outcome. A systematic diagram depicts a sequence of goals/ objectives and their respective means chained together so that we can visualize our possible alternatives with respect to the accomplishment of the high-level

Part V.B

Other Applications



Unintended line shutdown

Production error/defect

Part/supply shortage online

Part/supply failure at line

Line equipment failure

A

B

(Not developed)

(Not developed)

Part/supply out of stock

Part/supply order failure

Part/supply stocking failure

Certified vendor

Defective parts/supplies Vendor late delivery

Order issue— failure to outside

Order passback— failure within plant

Rack/bin unavailable Wrong location

Vendor out of stock Error in schedule

Lost passback card

Error in communication

Wrong part/supply

C (Not developed)

Racked/binned improperly

Line oversight

Late order

Figure 28.3

Simplified line shutdown fault tree.

Part V.B


goal/objective. The goal tree is very similar to the systematic diagram in that it is built around a high-level goal that we want to accomplish. It is also similar to the FT in that it links lower-level subgoals, functions, and success trees together with logic symbols or gates that lead up to the top goal. A concept fan is built in a tree format, but differs from the other formats significantly. The concept fan is a creativity-based tool, where we start with a purpose or functional requirement in a generic sense and expand it backward to provide alternate concepts that can accomplish the purpose or functional requirement. It is simple to construct and allows us to visualize possibilities for accomplishing our purpose early in the creative process.

Illustration A partial goal tree is illustrated in Figure 28.4. This tree structure uses AND gates to connect goals, subgoals, and functions. Success trees are then hooked into the functions using OR gates. The essence of the goal tree is to support strategic and tactical planning by depicting paths of goal accomplishment. For more information on tree-like diagrams, see Mizuno (1988), Brassard (1989), and Kolarik (1995, 1999).


Process methods Assembly technicians Robust design

Process flowchart Control chart

Load/unload SPC

Partial manufacturing cost improvement goal tree.

Process specs

Tools Product specs Experiment design

Scatter diagram

Machines

Machines Materials Basic statistics

Cause/effect diagram

Cutting fluid

Process training

Speed/feed

Product training

Improve technical knowledge

Improve incentives

Fixtures

Experimental program training

Improve TQM training

Improve training and knowledge

Tooling

TQM tools

Improve packaging

Improve processes

Improve employee effectiveness and efficiency

Fixtures

Run

Improve assembly

Improve production planning

Improve manufacturing practices

Reduce manufacturing costs, product XX

Tooling

Setup

Milling center

Part V.B


Figure 28.4

Success trees

Maintenance

Functions

Improve fabrication

Improve tolerance design

Improve design for manufacturing

Improve parameter design

Improve material handling

Improve system design

Subgoals

Goal




PROCESS DECISION PROGRAM CHARTS The purpose of a process decision program chart (PDPC) is to help people organize and evaluate process-related events and contingencies with respect to implementation and/or early operations.

Application The PDPC is useful in helping us to proactively evaluate or assess process implementation at a high level, early in the planning stage, or in the initial start-up phases of process operations. We may use the PDPC to argue or work our way through implementation, including events that might arise or occur and possibly disrupt our process and/or its implementation. Or we might use the PDPC to guide early operations in case of deviations from plan. The key use for the PDPC is to help us anticipate deviations from expected events and then help us to provide effective contingencies for these deviations. The PDPC can take several general formats. One format resembles an annotated tree diagram. Another format resembles an annotated process flowchart. In either case, the distinguishing mark of a PDPC is its ability to offer the user/reader an overview of possible contingencies regarding process implementation and/or operations.

Mechanics Although the PDPC can take one of several formats, several steps are common to all formats: 1. Identify the process purpose. Understanding the process purpose is critical to building and using the PDPC. This purpose will guide the PDPC development from the standpoint of possible contingencies and their resulting impacts relative to the desired outcome.

Part V.B

2. Identify the basic activities and related events associated with the process. Here, we use a tree or process flow format to place the activities in the expected sequence. This step should present a graphical depiction of activities that are part of the plan to be implemented and/or the basic operation. 3. Annotate the basic activities and related events. Working from step 2, we provide summarized descriptions of activities and events relative to what we normally expect to happen. 4. Superimpose the possible (conceivable) deviations. At this point, we add branches/events that represent identified deviations from the expected activities/events. 5. Annotate the possible deviations. We provide summarized descriptions relative to the deviations that have been mapped onto our chart in step 4.



6. Identify and annotate contingency activities. This step provides a description of the contingencies that we identify to avoid or counter the mapped deviations. 7. Weight the possible contingencies. At this final step, we examine the PDPC as a whole, consider the purpose, and select/mark the most appropriate contingencies. At this point we have a contingency plan—complete with our priorities for avoiding and/or dealing with possible deviations from our original implementation and/or operational plan.

Illustration A receiving/storage/stocking subprocess is depicted in the PDPC format in Figure 28.5. This depiction provides a basic look at the existing process, with several deviations indicated: damage, shortage, salvage, expedition, and line delay. It provides a number of facts and figures. Contingency-related issues are discussed in Table 28.1 relative to possible root causes and impact. In this case, general contingencies were process improvement, process redefinition, or the status quo subprocess. For more information on PDPCs, see Mizuno (1988), Brassard (1989), and Kolarik (1995, 1999).

MATRIX DIAGRAMS The purpose of a matrix diagram is to help people discover, visualize, and communicate relationships within a single set of factors or between two or more sets of factors.

Application

Part V.B

A matrix diagram typically is used to display relationships between two sets of characteristics or factors. However, it can be used to display interrelationships within one set of characteristics or factors. The typical layout is a two-dimensional matrix with the vertical dimension used to lay out one set of factors and the horizontal dimension used to lay out the other set. In the case of displaying interrelationships within one set of factors, the same factors are laid out in both the horizontal and vertical dimensions. We typically identify and document relationships within each set and between the two sets at intersection points in our graphic. The concept of a matrix diagram is relatively simple—essentially, we develop it to help us relate sets of factors or characteristics, usually in a qualitative fashion. The actual development of a matrix diagram, however, is rather involved in terms of defining level of detail, completeness, and association. Quantification and prioritization are addressed in the Prioritization Matrices section. In quality-related work, a primary application of the matrix diagram is to relate customer needs, demands, and expectations in the customer’s language to technical characteristics of the product/process, expressed in the producer’s



From production scheduling

To procurement

From procurement/ outside vendors

Inventory/ receiving entry

Unpacking/ inspection

Storage entry

Hot order

Shortage? Receiving/stocking team

Storage

Expedite?

Expedited delivery

Damage?

Line order

Inventory update

Storage retrieval

Order assembly

Storage subprocess

Receiving subprocess Unloading

To procurement

Excess back to storage

Line delay? Stocking subprocess Order delivery/ tracking

Expedite? Shortage?

Salvage? To fabrication To paint To assembly To product repair

Supply-side performance metrics

Customer-side performance metrics

Inbound deliveries: 137 Rush/airfreight deliveries: 43 Damaged shipments: 3 Short shipments: 5 On-time delivery: 53%

Outbound deliveries: 324 Expedited orders: 112 Line shortages: 63 Line delays (for lack of materials, assemblies, parts, supplies): 17 On-time delivery to line: 69%

To fabrication To paint To assembly To product repair

Primary suppliers

Supply-side demands (summarized)

Primary customers

Customer-side outcomes (summarized)

Procurement/ outside vendors

Right stuff: materials, assemblies, parts, supplies—meet all technical specifications, right amount, on time

Fabrication, paint, assembly, repair

Right stuff: materials, assemblies, parts, supplies—meet technical need, right amount, on time, in right place

Production scheduling

Assembly, part, supply orders: right item, right amount, right lead time, right due date to line

Procurement

Receiving report: right purchase order number, accurate count, condition, timely

Figure 28.5 Receiving/storage/stocking subprocesses PDPC.

Part V.B


language. Figure 28.6 illustrates this particular application of a matrix diagram. This illustration contains interrelationships in the triangular appendages at the left side and the top of the matrix. Here, we use “+” and “–” symbols to represent positive and negative relationships, respectively. We use the bull’s-eye, open circle, and triangle symbols to represent very strong, strong, and weak relationships between characteristics of the two sets, respectively. In this particular matrix diagram, we have included customer needs, demands, and expectations, technical definition characteristics, and competitor characteristics together. Matrix diagrams differ in scope and detail, as well as layout format. See Mizuno (1988), Akao (1990), Day (1993), and Kolarik (1995, 1999), for details regarding the


Our manufacturing time estimates that drive several parts of our master scheduling system were made using time estimates from our former/pre-redefinition product/ production processes. Our redefined product/production processes flow better than we anticipated/estimated— provided materials, assemblies, parts, and supplies are readily available. High Lift supplies centralized storage/ inventory system solutions to its customers. This concept is a part of High Lift culture and reflected in current operations. Centralized storage for all items is questionable.

2. Line flow is faster than the master schedule algorithm reflects in order issuance—why?

3. Everything that enters receiving goes through the storage area, with the exception of “hot” items that are needed to resolve a line shortage—why?

Part V.B

Centralized storage capital as well as operational costs are running about $1.2 million per year. Material, assembly, part, and supply obsolescence costs are running at about 5% of purchased part costs or about $2 million dollars per year. Potential customers are brought in to observe the technical operations of the High Lift storage system. This demonstration is viewed as a decisive element in customers choosing High Lift. Such observation is involved with about 45% of system sales.

A lack of current reality of our present redefined processes within our scheduling algorithm is holding our production process back from realizing its full potential. Present mismatches are putting brakes on potential assembly improvement on the lines. Estimated savings potential: $1 million to $10 million per year.

More prompt deliveries from our vendors could decrease our airfreight costs and reduce our line shortages, speeding up/smoothing out our assembly subprocess. Estimated savings potential: $1.1 million per year.

General impact


We do not communicate as well as we should with our vendors. Our vendors do not see High Lift as a large account. Our vendors are not capable of providing better service to us under current conditions.

Possible root causes

Issues, possible root causes, and general impact summary for receiving/storage/stocking PDPC.

1. Our vendors are not aware of, or responsive to, our line shortage problems—why?

Issue

Table 28.1



– : Strong negative

:3

–

+

–

* Priority quality characteristics

Priority scores

Easy to pay

Inexpensive

Return pocket contents

Fix clothes

38

3

*

73

3

38

3

*

64

3

56

9

56

9

25

*

97

15

15

27

3

9

35

*

76

15

21

5

33

3

15

15

18

3

15

+

39

9

15

15

*

61

15

21

25

+

33

15

3

15

Competitor A Low cost edge

Sales points—ours Cleaning and cust. ser. edge


*

73

3

35

25

15

+

Location edge

Quality function deployment matrix diagram.

+

Handy location

Convenience

35

+

+

Competitor B

Figure 28.6

+

+

Home pickup/delivery

–

35

+

Building location

–

+

+

Building access

–

25

7

+

Customer greetings

Friendly service

15

7

35

–

+

Customer relations

+

15

7

+

Business hours

5

21

Search pockets

Fast service

Press 35

Buttons

35

+

– – – –

Alterations

35

–

+

Time in line

+

nc 35

–

–

+

Payment methods

–

o er om st u c

Good looking clothes

Brightness 35

Smell

35

Spot removal

Clean clothes

–

–

+

Cleaning cycle time

–

p

im Demanded quality (customer language)

+ –

–

3

3

7

5

3

3

7

5

7

7

Relative weights—ours

–

+

of et

Cell scores are calculated as the product of the relative weight and the relationship value.

g e re ta or

:1

+ : Strong positive

Quality characteristics (technical language)

Laundry service De

:5

Relationships values are set as

Relative weights are set according to our proposed quality strategy.

Part V.B SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 302 Part V: Continuous Improvement



matrix diagram in general and specific quality function deployment (QFD) applications in particular.

Illustration Figure 28.6 provides a simplified matrix diagram regarding a laundry service. Ignoring the quantification numbers in the matrix for now, we see customer demands on the left and technical quality characteristics on the top. Two interrelationship matrices appear at the left and top. Customer degrees of importance and laundry sales points appear in vertical columns. Here, critical laundry sales points include clean clothes, good-looking clothes, friendly service, and return of pocket contents. This type of matrix diagram is commonly found in QFD work.

PRIORITIZATION MATRICES The purpose of a prioritization matrix is to help people measure/evaluate relationships from a matrix or tree analysis relative to a weighting scheme and decision criteria in order to set implementation priorities for the decisions at hand.

Application

Part V.B

Once we develop/identify relationships and options/alternatives such as might be developed through a relationship matrix, a relations diagram, or a tree diagram, or through some other means, we typically move into a decision mode. The prioritization matrix allows us to make relative comparisons and present our information in an organized manner so that we can support our decisions with consistent, objective, quantitative evaluation. Prioritization typically requires two things: (1) decision criteria and (2) a means of structuring relative comparisons. Decision criteria stem from our perception of what is important. For example, economics, timeliness, physical performance, and customer service form basic categories from which to develop decision criteria. Once developed, these criteria must be assessed as to their importance within the judgment of each decision maker and collectively between decision makers. This assessment may be carried out subjectively or objectively. In the subjective case, we as individuals draw on our past experiences and perceptions of the future and collectively use some sort of consensus/voting/ ranking-based process. Methods include various types of rating/voting schemes— the Delphi method, the nominal group technique, and other methods. The matrix diagram illustration in Figure 28.6 contains two sets of rankings, one for the strength of the relationships in the body and one for the relative importance of our sales points. Together these two sets allow us to quantify the body of the matrix and develop quality characteristic scores. Hence, we can prioritize our thinking/ action in terms of the more critical quality characteristics. In this case, criticality is indicated by the “*” symbol, and a total score of 60 (selected subjectively) was used as the criteria for selection. In the objective case, we assign relative weighting values and quantitatively manipulate these values to converge to a relative priority number. Several techniques exist for objectively establishing prioritization criteria. The analytical



hierarchy process (AHP) is widely described as a quantitative technique (Saaty 1982). The AHP allows a number of decision makers to integrate their priorities into a priority matrix where the decision criteria are compared as to relative importance in a pairwise fashion. The results include a decision criteria priority matrix and a corresponding alternative priority-weighted matrix. Hence, a quantitative group consensus analysis matrix emerges. From this analysis, the alternatives can be selected with the confidence that all criteria—economic, technological, and intangible factors—are integrated into the decision process. For more information on prioritization matrices, see Brassard (1989).

ACTIVITY NETWORK DIAGRAM AND ARROW DIAGRAM The purpose of activity network diagramming is to help people sequentially define, organize, and manage a complex set of activities and events with respect to time schedule planning and implementation. The Japanese scheduling/planning tool known as an arrow diagram is a hybrid derived from Gantt chart technology and a simplified extraction from the program evaluation research technique (PERT) and critical path method (CPM) technologies (see Kolarik [1995] and Mizuno [1988]). An arrow diagram is a network planning method that displays activities on the “arrows” as opposed to on the “nodes.” An activity network diagram is a derivation of PERT, CPM, and the arrow diagram (Brassard 1989). We will describe a useful simplified version of CPM with activities on the nodes.

Part V.B

Application Complex processes typically are made up from a number of activities that must be carried out in a defined sequence in order to accomplish the desired result. We use an activity/sequence list to identify and organize a set of activities as to sequence and estimated duration. In general, each activity involved with an endeavor will be sequential, parallel, or coupled to other activities. Sequential activities require that a predecessor activity be completed before its successor can begin. Parallel activities can be undertaken and executed simultaneously. Coupled activities are executed together and hence their progression is linked together in some manner. The activity/sequence list addresses these relationships. First, each activity on the list is uniquely identified. Then, the sequence as to predecessor and successor activities is established. Finally, we estimate duration for each activity. An example activity/sequence list appears in Table 28.2. From the activity/sequence list we construct CPM-like networks of our planned activities, allowing us to organize and display a schedule of project activities/events with regard to starting and finishing time estimates—both as a whole project and as individual activities. In order to develop a CPM network for a project, we first identify activities and events. An activity is something that requires action of some type such as shingling a roof. An event happens at a specific time, for example, the beginning or ending point of an activity. Our critical events represent milestones—points



Table 28.2

Line support improvement process activities, sequences, and durations.

Activity description

Activity symbol

Predecessor

Duration, days

Explain change/plan to affected areas Identify rackable/binnable items Design racks/bins/storage facility modifications Build/test racks/bins* Identify/inform affected vendors

A B C D* E

A B C A

2 7 21 14 4

Prepare High Lift and vendor training/certification materials Gain vendors’ cooperation Certify/train vendors* Review/modify High Lift team needs Train High Lift people*

F G H* I J*

A E G, F F I

15 8 10 2 5

Modify staging facilities Modify in/out facilities Develop procurement scheduling/card system* Rack/bin existing bulk inventory*

K L M* N*

C C F D, K, L

12 14 25 14

Stage racks/bins to line Remove/salvage old storage area Limited-scale operation, test/tune/mistakeproof* Full-scale operations

O P Q* R

N O H, J, M, O Q

5 20 30 –

*Indicates milestone activities; milestone occurs at the end of the marked activity. Reproduced with permission from W. J. Kolarik, Creating Quality: Process Design for Results (New York: McGraw-Hill, 1999): 474.

at which we reassess our progress. The activity/sequence list is a helpful tool to summarize activities, sequences, and time estimates.

Mechanics

Part V.B

A CPM-like network diagram is depicted in Figure 28.7. Here, we have taken the symbol, sequence, and duration information from our activity/sequence list in Table 28.2. The network flows from left to right in a time sequence. Each activity is represented by a node, that is, a circle. Within each circle we list the activity’s symbol and its estimated time duration. Other information developed includes earliest start time ES, earliest completion time EC, latest start time LS, and latest completion time LC. These estimates are provided for each node/activity on the network. The critical path is defined as the path that determines the minimum completion time for the entire project. Bold-faced arrows usually depict the critical path. If a delay occurs on any activity on the critical path, then the project duration will be increased. Hence, we watch the activities on the critical path very carefully with respect to time duration violations. The ES and EC estimates are developed on a forward pass through the network of activities and durations. We develop the network using a start event and



6

14

17

G 8 2

6

45

27 H 10

53

63

53

E 4 41

17 45 17

2

38

F 15

0

0

0

2

0

0

9

30

B 7 2

2

9

9 C 21 9 ES

EC A t

LS

LC

Figure 28.7

63 63

19

30 30

A: Activity

19

24

58

58

93 Q 30

63

J 5

I 2 56

A 2

Start 0

2

17 38

23

42 M 25

93 93

63 63

44 44

L 14 30 44 30 42 K 12 32 44 30 D 14 30

58

44

58

58

58

83 P 20

63 O 5

N 14

93 R

93

73

93 93

93

Finish 93 93

93

63

44 44

LS: Latest starting time

t: Activity duration (days)

LC: Latest completion time

ES: Earliest starting time

Critical path

EC: Earliest completion time

Milestone activity

Simplified CPM schedule network–line support improvement implementation.

Part V.B


a finish event. We usually start at time zero, and finish at the shortest time possible, considering our time/duration estimates. On the forward pass, we begin at the start node and develop our ESj estimates for each node. Usually, we assume the ESStart node is equal to zero. However, we could assume some positive value. Then, we develop ESj estimates for each activity as we move from left to right (across time) through the network. Each ESj is equal to the maximum of the ECi estimates taken from the set of all immediate predecessor activities. Each ESj is estimated by summing its ESj and its duration, t j. The ECFinish node is equal to the maximum of the ECi estimates taken from the set of all immediate predecessor activities. The LC and LS estimates are developed on a backward pass through the network of activities. Starting at the finish node, we set the LCFinish node equal to the ECFinish node. We set the LSFinish node equal to the LCFinish node. We estimate LCj as the minimum of the LSi estimates taken from the set of all immediate successor activities. Each LSj is equal to its LCj minus its activity duration, t j. Tables such as Table 28.3 are constructed in order to both facilitate our network development as well as summarize our results. We usually repeat our activity descriptions, symbols, and durations. We list our ES, EC, LS, and LC estimates, which match those in our CPM network. Additionally, we include total slack, TS, and free slack, FS, estimates. In the CPM network method, we define total slack as



Table 28.3

Line support improvement scheduling details.

Activity description

Activity Duration, symbol days ES EC LS LC TS FS Critical?

Explain change/plan to affected areas Identify rackable/binnable items Design racks/bins/storage facility modifications Build/test racks/bins* Identify/inform affected vendors

A

2

0

2

2

0

0 Yes

B C

7 21

2 9 9 30

0 0

0 Yes 0 Yes

D* E

14 4

30 44 30 44 2 6 41 45

0 39

0 Yes 0 No

Prepare High Lift and vendor training/certification materials Gain vendors’ cooperation Certify/train vendors* Review/modify High Lift team needs Train High Lift people*

F

15

2

19

0 No

G H* I

8 10 2

6 17 17

14 45 53 39 3 No 27 53 63 36 36 No 19 56 58 39 0 No

J*

5

19

24

Modify staging facilities Modify in/out facilities Develop procurement scheduling/ card system* Rack/bin existing bulk inventory*

K L M*

2 9 9 30

17

0

21

36

58 63

39

39 No

12 14 25

30 42 32 44 30 44 30 44 17 42 38 63

2 0 21

2 No 0 Yes 21 No

N*

14

44

58

0

0 Yes

Stage racks/bins to line Remove/salvage old storage area

O P

5 20

58 63 63 83

58 63 73 93

0 10

0 Yes 10 No

Limited-scale operation, test/ tune/mistake-proof* Full-scale operations

Q*

30

63

93 63

93

0

0 Yes

93

93

93

0

0 Yes

R

58 44

93

*Indicates milestone activities; milestone occurs at the end of the marked activity. Reproduced with permission from W. J. Kolarik, Creating Quality: Process Design for Results (New York: McGraw-Hill, 1999): 476.

the amount of time activity j may be delayed from its earliest starting time without delaying the latest completion time of the project.

Whenever the TSj equals zero, we have a critical path activity. Free slack is defined as the amount of time activity j may be delayed from its earliest starting time without delaying the starting time of any of its immediate successor activities:

Part V.B

TSj = LCj – ECj = LSj – ESj

FSj = Min {(ESi = 1 – ECj), (ESi = 2 – ECj), …, (ESi = Last successor activity – ECj)} where i corresponds to the index for all successor activities, i = 1, 2, . . . , last successor for activity j. We can use updated CPM graphics and tables to update our plan as activities are completed. Additionally, we can project changes in subsequent activity



estimates. Here we use the same basic rules that we used to develop the initial CPM network, but begin at the end of the completed event. Hence, we can generate updated ES, EC, LS, and LC estimates for the remaining activities, as well as redevelop our slack estimates. We also can determine if our critical path has changed as a result of our changes. Additional details pertaining to project planning and implementation are available in project management texts, such as Badiru and Pulat (1995).

PROCESS MAPS The purpose of a process map is to help people discover, understand, and communicate the input-to-transformation-to-output characteristics of a process.

Application Process flowcharts are used to map processes at any level of detail. Gross-level maps are useful in high-level planning work, while minute-level maps are useful in process control work. A flowchart depicts process flow by using a sequence of symbols and words to represent process flow components—all connected with directional line/arrows to indicate flow paths. A wide variety of processes are charted, and hence a wide variety of symbols are used. In some cases, simple box or rectangular symbols are used that are self-descriptive or annotated near the symbol. In other cases, the symbols are iconic in the sense that the symbol shape is indicative of the process element. Usually, a legend is provided to define specialized symbols. Typically, the more focused the flowchart, the more specialized the symbols. Process mapping is performed by teams and individuals—operators, technicians, engineers, specialists, and/or managers. Diverse perspectives are gained through process mapping when an interdisciplinary team is involved with the mapping. See Kolarik (1995) for general details, Barnes (1980) for specialized charting techniques relative to classical industrial engineering, and Hughes (1995) for automatic process control–related flowcharting basics.

Part V.B

Mechanics We map processes to help us understand how processes work, or how they are expected to work. Process flow mapping usually involves several steps. 1. Establish flowchart/map purpose. Initially, we clearly state the purpose for our charting efforts. This purpose will dictate the level of detail we need in our map. 2. Define map boundaries. We determine the starting and ending points for the mapping effort, relative to purpose and necessary observations. 3. Observe process. Next, provided the process is in operation, direct process observation/experience is necessary to develop the process map. We may also observe/map processes in other organizations through benchmarking activities.



4. Establish gross process flow. Here we develop/chart a process overview, depicting the production system or process in terms of major components, for example, processes or subprocesses, respectively. 5. Develop map details. Once we have obtained and captured the general essence of the process flow, we focus on details, cascading the level of detail down to the point where it is compatible with our purpose. Details are sequenced to represent the order/position that they occupy in the actual process. 6. Check for validity/completeness. Finally, we move from level to level in our maps—we examine our maps for validity and completeness. Validity checks typically involve map review as to accuracy of inputs, transformation, output, and sequence. Completeness extends to the level of detail within the target process as well as interactions with other processes.

Illustration Figure 28.8 provides an illustration of a macro-level process map, broken out by the seven fundamental processes—market/definition, design/development,

Process Type

Processes/Subprocesses

Market/ definition

Customer solutions definition— customer process characterization

Design/ development

Customer solutions/preliminary alternatives/modeling

Production Receiving

Distribution/ marketing/sales/ customer service

Customer training

Information reporting, record keeping

Procurement

Public relations

Figure 28.8

Assembly

Customer account services

Sales and post-sales service

Field support parts/maintenance

Business planning and budgeting

Product repair

Accounts payable, receivables

Product test/checkout

Facility and equipment maintenance

Product shipping

Disposal/recycle information

Financial and cash management

Payroll/human resources/benefits

Safety/health/ environmental compliance

Modular process

Modular processes are characterized by primarily self-contained operations, provided clear input/output requirements are defined

Integrated process

Integrated processes are characterized by cross-functional or service operations, where multiple sets of customers and input/output requirements are defined

Part V.B

Business integration

Promotion/ product information

Paint

Line stocking

Customer solutions/system concepts/specifications

Customer solutions/field engineering

Customer solutions/final design/model demonstration

Storage/materials/ assemblies/parts/supplies

Fabrication

Use/support Disposal/ recycle

Customer solutions assessment—customer need/demands/expectations determination

Enterprise-level process map.




production, distribution/marketing/sales/service, use/support, disposal/recycle, and business integration. Here, we can see a global depiction of the essential processes involved in an enterprise. We can “drill down” through these fundamental processes and build more detailed process maps, sometimes resembling a PDPC in nature. Figure 28.9 provides an illustration of such a map for a visual manufacturing alternative subprocess plan. Process maps may be layered to depict a process hierarchy; see Kolarik (1999) for details.

To vendors

Procurement team

Vendor order/cards

Inventory/ receiving entry

Line order/ cards

Production scheduling

Temporary rack/bin storage

Order delivery

To business integration

Unloading From/to outside vendors

Verify arrivals

Receiving/storage subprocess Loading

Procurement subprocess

Qualify/certify vendors

Expedite?

Document defectives

Inventory/ defective entry

Supply-side performance metrics Present Inbound deliveries: 137 Rush/airfreight deliveries: 43 Damaged shipments: 3 Short shipments: 5 On-time delivery: 53%

Part V.B

Customer-side performance metrics Present Outbound deliveries: 324 Expedited orders: 112 Line shortages: 63 Line delays (for lack of materials, assemblies, parts, supplies): 17 On-time delivery to line: 69%

Figure 28.9

Receiving/stocking team

Targets Improved 0 0 0 100% Targets Improved 0 0 0 100%

Shortage?

Empty return

Salvage? To business integration

Line orders

Stocking Order staging subprocess at line

Damage? Temporary rack/bin storage

From fabrication From paint From assembly From product repair

Line delay? At fabrication At paint At assembly At product repair

Control metrics Downstream Expedited orders Line shortages Line delays On-time delivery to line Obsolete inventory parts/dollars Upstream Deviation to due date (by vendor/stocking) Overtime hours in receiving/ stocking Defectives returned to vendors Vendor certificates/progress/ maintenance Design change/inventory coordination

Targets 0 0 0 100% 0.05%

±1 0 0 100% 100%

Visual alternative–improved subprocess map/PDPC.




PROCESS VALUE CHAIN (PVC) DIAGRAMS The purpose of a value chain diagram is to help people depict and understand a sequence of cause-to-effect and effect-to-cause relationships between business results and outcomes and basic physical, economic, and social variables.

Application PVC analysis links basic physical and social variables with business results so that value-added process sequences are clearly depicted. This linkage is not precise because each basic variable has its own natural/technical units of measure, for example, length, pressure, volume, or composition, while process/business results are expressed in their own units or unitless ratios, for example, production units, percent conformance, scrap rate, efficiency, cost, revenue, profit, and return on investment. Hence, PVCs have discontinuities where unitary incompatibility presents gaps and challenges. The point is to link variables related to specific process decisions and process control points to business results and vice versa as best we can. Hence, understanding as to cause–effect and time lags in moving from cause to effect become more obvious for all concerned, for example, operators, engineers, and managers. The PVC diagram connects the business world to the technical world through a logical, sequential linkage that cascades up and down all processes and their respective subprocesses. PVC diagrams are useful for operators to see how operational decisions in the technical world ultimately impact business results. They are useful for managers/leaders to clearly see that business targets are met through a sequence of operational decisions. An efficient and effective PVC adds value to products throughout the chain.

Illustration

VALUE STREAM MAPPING

Part V.B

A generic PVC is depicted in Figure 28.10. Across the top we see basic business outputs on the right-hand side and basic inputs in the form of controlled and uncontrolled variables on the left-hand side. Transformations in the form of processes and subprocesses are depicted in the middle. The oval cycle on the bottom half of the figure shows that we develop a PVC working from one of several starting points—we may start somewhere in our outputs, the business results, and work toward inputs, the basic variables. Or, we may start somewhere in our input variables and work toward our business outputs. The focus is to understand how the processes work, and how they impact the business objectives. See Kolarik (1999) for more details regarding PVC.

A value stream map is similar to a flowchart, but includes additional information about various activities that occur at each step of the process. Value stream mapping (VSM) is a powerful tool based on the principles of lean and used to identify opportunities for improvement of a process and track performance. Currentstate value stream maps provide information about the process as it is currently defined. Future-state value stream maps provide information about the process



Process action Uncontrollable variables

Subprocess (Subprocess purpose)

Process (Process purpose)

Control, stability Subprocess results Targets and benchmarks Controllable variables

(a)

Production system (Vision, mission, core values)

Control, stability Process results Targets and benchmarks

Business results Targets and benchmarks

Process counteraction—Feedback/feedforward

Uncontrollable variables

Process act ion Time del ay

Controllable variables Cause–effect relationships

Time

ction —Feedb ack/feedforward

Response variables Re

su lts tar

tera

delay

ts ge

c Pro

es sc oun

Customer demands/expectations

(b) Figure 28.10

Generic production system process value chain diagram. (a) Analytical view. (b) General systems view.

Part V.B


as it could look once it has been redefined. Figure 28.11 displays a generic value stream map for a manufacturing process. Symbols used for a value stream map vary somewhat in the literature, but detailed examples can be found in Manos (2006) and Montgomery (2009b).

SIPOC DIAGRAMS SIPOC is an acronym for: • Suppliers. Those who provide inputs to the process including materials, resources, services, information, and so on. • Inputs. Materials, services, resources, information, and so on. • Process. Process description and listing of all key process steps.



Orders

Orders Refinery

Supplier

Customer

Inventory

Cleaning process

1 day

60–80 days

Figure 28.11

Inventory Expeller

Refinery

10–20 days

Value stream map for a manufacturing process.

• Outputs. Products, information, services, and so on. • Customers. Those who receive the outputs. Customers may be internal or external. The SIPOC diagram is a high-level process map used to identify the important aspects of the current process such as the process outputs and customers in order to capture the voice of the customer. It is a useful tool in the early stages of the DMAIC process to determine the critical-to-quality factors (see Chapter 29 for discussion of the DMAIC methodology). Figure 28.12 displays a simple SIPOC diagram for the process used to report and investigate work-related injuries at a manufacturing firm.

The purpose of benchmarking is to help people learn from the work of others— seek out, study, and emulate the best practices associated with high performance/ results—so as to enhance or better their own performance.

Part V.B

BENCHMARKING

Application We have a tendency to perceive our organization as the “best” through rather subjective arguments, for example, exhortations of various types. In reality, our perceptions may not be accurate. We may lack insight as to what is happening around us—what others are doing and the results they obtain. In essence, we lack



Suppliers

Inputs

Person injured Witnesses Co-workers Medical personnel Technicians/engineers Management

Accident information Work history Causes Equipment information Site information Site history

Process Accident occurs

Outputs

Customers

Complete report Indentification of cause(s) Corrective action plan Solutions implemented

Employees Management Health/safety personnel

Submit report with 48 hours Detailed investigation Develop corrective action plan

Implement corrective action plan

Submit final report Complete follow-up

Figure 28.12

SIPOC diagram for work-related injuries.

outside standards/benchmarks from which to judge our own performance. Benchmarking helps us to gain an awareness of shortfalls in our own performance, as well as to plan and implement countermeasures to enhance our performance. Informal benchmarking is a matter of natural curiosity and always has been practiced; however, formal benchmarking was positioned as an organizational initiative at Xerox. Kerns and Nadler (1992) defined benchmarking as: . . . the continuous process of measuring products, services, and practices against the toughest competitor or those companies recognized as industry leaders. Camp (1989, 1995) defines benchmarking as:

Part V.B

. . . the search for and implementation of best practices. Benchmarking encompasses four aspects: (1) analyze the operation, (2) know the competition and industry leaders, (3) incorporate the best of the best, and (4) gain superiority. The formal scope of benchmarking includes products, processes, and performance metrics. Camp cites four types of benchmarking: internal, competitive, functional, and generic. Internal benchmarking focuses on best practices within our own organization. Competitive benchmarking provides a comparison between direct competitors. Functional benchmarking refers to comparisons of methods across organizations executing the same basic functions outside our industry. Generic process benchmarking focuses on innovative work processes in general, wherever they occur.



Benchmarking, which was also discussed in Part I, is a broad initiative. Watson (1993) describes the evolution of benchmarking in terms of generations. He cites reverse engineering as the first generation. Here we see essentially a rote copying strategy. The second generation is termed competitive benchmarking, which focuses on direct competitors. As the third generation he cites process benchmarking, where processes common to different industries are assessed for best practices. The fourth generation is termed strategic benchmarking. Here, the focus is on the strategies that a competitor or noncompetitor uses to guide their organization. The fourth level is used to feed process reengineering initiatives. A futuristic fifth level is cited as global benchmarking. Here, the focus is international in scope and deals with trade, cultural, and business process distinctions among companies. In all cases, the driving force is “profit-oriented,” as addressed through three parameters: (1) quality beyond that of competitors, (2) technology before that of competitors, and (3) costs below those of competitors. The benchmarking initiative focuses on two basic issues: (1) best practices, and (2) metrics or measurement. We recognize performance gaps and address them with improvement plans. Management commitment, communication, and employee participation are all critical elements in a benchmarking initiative. For more information on benchmarking, see Camp (1989, 1995), Kolarik (1995, 1999), and Watson (1993).

Mechanics 1. Preplan the benchmarking initiative. Assess and understand customer needs and the business results/outcomes desired. 2. Plan and execute the initiative. Identify comparative organizations and what is to be benchmarked. Determine data collection methods and collect data. 3. Analyze the data and information collected. Determine the current performance gap. Project future performance levels/goals into the future. 4. Integrate the information into actionable issues. Communicate the findings and gain acceptance within your organization. Establish functional goals that are actionable.

6. Gain maturity in benchmarking. Attain a leadership position and integrate benchmarking practices into processes.

Part V.B

5. Prepare for action and act. Develop action plans, implement specific actions, monitor progress, and recalibrate the benchmarks.

Benchmarking clearly is an invaluable asset in quality improvement work. It provides a perspective of how things are done within other organizations and leads to the identification of best practices and encourages the adoption of the same. However, a best practice today will undoubtedly be eclipsed by a better practice in the near future. In many cases, we inject creative elements within/beyond current practices. These extensions require creative thinking or breakthrough thinking. Breakthrough



Benchmarking Our operation Short-term focus Noncompetitor’s operation Improvement need

Competitor’s operation

Focusing Awareness

Improvement

Purpose

Planning

Acting

Following through

Strategy

Breaking through Product

Long-term focus Service

Figure 28.13

Technology

Benchmarking and breakthrough thinking.

Part V.B


thinking typically is approached very differently from benchmarking. Here, we are encouraged to think “out of the box,” as opposed to thinking “in the box” (for example, finding an existing best practice). Creative thinking is the only tool available that allows us to move beyond best practices. Creativity has received considerable attention in quality improvement work (Kolarik 1995, 1999). Nadler and Hibino (1994) proposed seven principles of breakthrough thinking: (1) the uniqueness principle, (2) the purposes principle, (3) the solution-after-next principle, (4) the systems principle, (5) the limited information collection principle, (6) the people design principle, and (7) the betterment timeline principle. DeBono (1992) encourages the use of hats in creative thinking: the white hat—data and information, the red hat—feelings, intuition, hunches, and emotions, the black hat—pessimistic perspective, the yellow hat—optimistic perspective, the green hat—creative effort, and the blue hat—thinking process control. Figure 28.13 depicts an overview of the integration of breakthrough thinking into a benchmarking model. From this depiction, we can see that breaking through focuses on the essence of the product, technologies (relative to both products and process), and services. The long-term focus of breakthrough thinking tends to complement the shorter-term focus of benchmarking, yielding a broad view of improvement efforts.

SUMMARY OF QUALITY MANAGEMENT AND PLANNING TOOLS The concept of quality and our basic understanding of it have expanded and matured over the years to the point at which we now understand and appreciate the customers’ role in defining the essence of quality in every product. We now see our role (as leaders, managers, engineers, and operators) as creators of quality. Hence, the process approach to creating quality has become dominant.



A number of quality-related initiatives and tools have been generated and touted over the past several decades. Their names come and go, but the fundamental issues of understanding and responding to our physical, social, and economic environments in terms of customer needs and expectations remain. In summary, initiatives and tools that are both effective and efficient in helping us to discover, explore, understand, plan, and act in the best interest of customers and stakeholders will always be useful.

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Chapter 29 C. Continuous Improvement Techniques

Define, describe, and distinguish between various continuous improvement models: total quality management (TQM), kaizen, plan–do–check–act (PDCA), Six Sigma, theory of constraints (TOC), lean, etc. (Analyze) Body of Knowledge V.C

Quality improvement is achieved by continuously improving the production and business processes of an organization (Besterfield 1999). It is optimized by: • Viewing all work as a process, whether it is associated with production or business activities • Making all processes effective, efficient, and adaptable • Anticipating changing customer needs • Controlling in-process performance using metrics such as scrap and cycle time, and monitoring tools such as control charts • Maintaining constructive dissatisfaction with the present level of performance

Part V.C

• Eliminating waste and rework wherever it occurs • Investigating activities that do not add value to the product or service, with the aim of eliminating those activities • Eliminating nonconformities in all phases of everyone’s work, even if the increment of improvement is small • Using benchmarking to improve competitive advantage • Innovating to achieve breakthroughs • Holding gains so there is no regression • Incorporating lessons learned into future activities 318


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 29: C. Continuous Improvement Techniques 319

• Using technical tools such as statistical process control (SPC), experimental design, benchmarking, quality function deployment (QFD), and so on Continuous process improvement is designed to utilize the resources of the organization to achieve a quality-driven culture. Individuals must think, act, and speak quality. An organization attempts to reach a single-minded link between quality and work execution by educating its constituents to continuously analyze and improve their own work, the processes, and their work group (Langdon 1994). Process improvement achieves the greatest results when it operates within the framework of the problem-solving method. In the initial stages of a program, quick results are frequently obtained because the solution is obvious or an individual has a brilliant idea. There are a number of models for quality improvement. We will discuss total quality management (TQM), kaizen, PDSA (sometimes known as PDCA), reengineering, Six Sigma, theory of constraints (TOC), and the lean enterprise model.

TOTAL QUALITY MANAGEMENT (TQM) This term, originally used at the Naval Air Systems Command, encompasses the following ideas: • Customer focus. The customer determines whether a product or service is good enough. • Employee empowerment. All employees must understand that continuous improvement is a part of everyone’s job. • Leadership. Upper management must provide the impetus and motivation for the quality programs. This model, properly implemented, often results in an enterprise that is more productive and more competitive. Customer loyalty will improve and stakeholder value will increase.

KAIZEN

Part V.C

Kaizen is a Japanese word for the philosophy that defines management’s role in continuously encouraging and implementing small improvements involving everyone. It is a method of continuous improvement in small increments that makes processes more efficient, effective, under control, and adaptable. Improvements are usually accomplished at little or no expense without sophisticated techniques or expensive equipment. Kaizen focuses on simplification by breaking down complex processes into their subprocesses and then improving them. The kaizen improvement focuses on the use of: 1. Value-added and non-value-added work activities. 2. Muda, which refers to the seven classes of waste—overproduction, delay, transportation, processing, inventory, wasted motion, and defective parts. 3. Principles of motion study.



4. Principles of materials handling. 5. Documentation of standard operating procedures. 6. The five S’s for workplace organization, which are five Japanese words that mean proper arrangement (seiko), orderliness (seiton), personal cleanliness (seiketso), cleanup (seiso), and discipline (shitsuke). Various authors have translated them slightly differently. The National Institute for Standards and Technology (NIST) through the Manufacturing Extension Partnership uses sort, set in order, shine, standardize, and sustain. 7. Visual management by means of (visual) displays that everyone in the plant can use for better communications. 8. Just-in-time principles to produce only the right units in the right quantities, at the right time, and with the right resources. 9. Poka-yoke to prevent or detect errors. 10. Team dynamics, which include problem solving, communication skills, and conflict resolution (Gee, McGrath, and Izadi 1996). Kaizen relies heavily on a culture that encourages suggestions by operators who continually try to incrementally improve their job or process. An example of a kaizentype improvement would be the change in color of a welding booth from black to white to improve operator visibility. This change results in a small improvement in weld quality and a substantial improvement in operator satisfaction. The PDSA cycle, described next, may be used to help implement kaizen concepts.

Part V.C

PDSA OR PDCA The basic plan–do–study–act (PDSA) cycle, sometimes known as the plan–do– check–act (PDCA) cycle, was developed by Shewhart and is an effective improvement technique. It is sometimes called the Shewhart cycle or the Deming cycle. Figure 29.1 illustrates the cycle. The four steps in the cycle are exactly as stated. First, plan carefully what is to be done. Next, carry out the plan (do it). Third, study the results—did the plan work as intended or were the results unexpected? Finally, act on the results by identifying what worked as planned and what did not. Using the knowledge learned, develop an improved plan and repeat the cycle. The PDSA cycle is a simple adaptation of the more elaborate problem-solving method discussed in the next section.

REENGINEERING According to Hammer and Champy (1993), reengineering is the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical measures of performance. Many practitioners believe that quality improvement is associated only with incremental improvements. Nothing could



P L

Feedback assessment and corrective action

Product and process validation

on tc s u c es P r o d proc and

ST

U Figure 29.1

Plan and define Product design and development

Process design and development

fi va r m a li d t i o n a ti on

D Y

c ro t t/ P oto c u P r o d n d pr a

O

im Co pr

t

t ep nc co nt

en in nt vem o

Techn ol dev ogy a elo n pm d e

e yp ss d e e ve velo p m r ifi c e nt a ti o n

T C ual

N A

A

Chapter 29: C. Continuous Improvement Techniques 321

D

Basic plan–do–study–act cycle.

be further from the truth—for many years the Malcolm Baldrige National Quality Award has defined continuous improvement as referring to both incremental and “breakthrough” improvement. The Japanese have not only relied on kaizen but have developed policy management (hoshin kanri) and policy deployment (hoshin tenkai) in large part to produce the kind of large-scale breakthroughs that Hammer and Champy promote. Nor is this concept uniquely Japanese. Joseph Juran had a long-standing emphasis on breakthrough efforts aimed at achieving unprecedented levels of performance. Clearly there is nothing new in the reengineering concept—it has always been part of the total quality management (TQM) umbrella.

According to Senge (1990), most organizations die before they reach the age of 40. Companies are embracing Six Sigma not only to reduce defects but also as a catalyst to change the culture of their company and impact how employees engage in their everyday work. Utilizing a Six Sigma business strategy, organizations can understand threats and recognize new opportunities for growth, not only to survive but to actually thrive within competitive environments.

Part V.C

SIX SIGMA



Quality practitioners often note that the tools of Six Sigma are not unique. It is true that most Six Sigma techniques are familiar; however, the power of properly integrating them as a total system is new. Six Sigma creates a road map for changing data into knowledge, resulting in process-focused change and bottom-line benefits for organizations. Not all organizations have achieved success with Six Sigma, which depends on the successful integration of two components: strategy and metrics. The strategy of Six Sigma relates to how the methodology (tools and techniques) is integrated into an organization through key projects, yielding substantial benefits to an organization’s bottom line. Companies experiencing success with Six Sigma have created an effective infrastructure for selecting, supporting, and executing projects. These projects are focused on achieving strategic business goals, as well as addressing the voice of the customer. The success of Six Sigma also depends on the wise application of metrics. Unfortunately, much confusion exists relative to the metrics of Six Sigma. There is no “one size fits all” metric applicable to every project. Effective metrics are crossfunctional, providing a holistic view of the process and contributing insight to the project team. A lot of resources are wasted if Six Sigma metrics are not applied wisely and subsequently used to orchestrate improvement activities. “Fire prevention” is preferred to “firefighting.” This section details the two components previously mentioned, as well as other important aspects of a successful Six Sigma implementation, including the following: • Six Sigma needs assessment • Six Sigma as a business strategy • Implementing Six Sigma • The metrics of Six Sigma • Sustaining and communicating change

Part V.C

Six Sigma Needs Assessment Organizations often become overwhelmed with day-to-day activities and lose sight of what needs to be done to make process-focused improvements or reengineering changes in order to survive the “long haul.” Individuals within organizations might be aware of Six Sigma and think that the techniques could be useful to reduce the amount of firefighting activities that occur; however, they may have trouble determining where it applies and where the benefits are achievable. This type of organization requires a simple and quick approach to make a Six Sigma needs assessment. For this situation, we suggest that people within the organization respond to the Six Sigma Needs Checklist shown in Table 29.1. Upon completion of this survey, the additional question can then be asked, “How much money are the affirmative responses costing the business annually?” An improvement opportunity often can be accurately quantified if the amount is initially determined as a percentage of the gross revenue of the organization.



Table 29.1 Six Sigma needs checklist. Six Sigma needs checklist

Answer yes or no

Do you have multiple “fix-it” projects in a critical process area that seem to have limited or lasting impact? Are you aware of a problem that management or employees are encountering? Are you aware of any problem that a customer is having with the products/services your organization offers? Do you believe that primary customers might take their business elsewhere? Is the quality from competitive products/services better? Are your cycle times too long in certain process areas? Are your costs too high in certain process areas? Do you have concerns that you might be “downsized” from your organization? Do you have a persistent problem that you have attempted to fix in the past with limited success? Do you have regulatory/compliance problems? Source: F. W. Breyfogle III, J. M. Cupello, and B. Meadows, Managing Six Sigma (John Wiley & Sons, 2000). Adapted by permission of John Wiley & Sons, Inc.

Part V.C

Monetary estimates from this survey could be considered the perceived “cost of doing nothing” within the organization. That is, the cost to the business of not “doing Six Sigma.” When this survey is conducted during a meeting of informed individuals, an even more accurate estimate for this cost can be obtained. We suggest that during this meeting, individuals describe the logic used for their vote. Consensus might then be achieved for an overall monetary estimate for the group. When consensus does not seem possible, an average of the responses can give a very good estimate. Estimated projected benefits from Six Sigma could then be approximated as 25 percent to 50 percent of the projected monetary “cost of doing nothing.” Experience has shown that full-time Six Sigma Black Belts can save on the average of $500,000–$1,000,000 annually, depending on: • Executive-level support • Process focus area (that is, some areas have more room for improvement than others) • Team motivation • Six Sigma Black Belt (that is, Six Sigma practitioner) proficiency



Part V.C

Six Sigma As a Business Strategy A question we frequently hear from executives is, “How does Six Sigma fit with other corporate initiatives?” Six Sigma should not be considered just another initiative but should integrate other programs (for example, lean manufacturing and kaizen) at a higher level as part of an overall business strategy. Six Sigma should not replace other initiatives but instead create an infrastructure that offers a tactical approach to determine the best solution for a given process/situation. Successful implementation should be viewed as an ongoing process of infusing the Six Sigma methodology into the way your employees approach their everyday work. It requires a proactive view and the commitment to evolve into a more process-oriented culture and reduce the amount of daily firefighting on strategic processes. The implementation process requires up-front work to develop awareness and generate buy-in before projects are selected. This process often displays unique characteristics in each organization; however, there are two essential elements needed for success: executive leadership and customer focus. To date, companies achieving significant results with Six Sigma have the commitment of their executive management. Executive leadership is the foundation of any successful Six Sigma business strategy. Upper managers need to develop an infrastructure to support the changes that implementing Six Sigma will create, not only to strategic business processes but also, as previously discussed, to the culture of the organization. Past quality programs resulted in varying success because they typically did not have an infrastructure that supported change. The results received from a Six Sigma business strategy are highly dependent on how well leaders understand the value of wise implementation of the methodology and sincerely promote it within their organization. An executive retreat can help identify true champions that will promote change and can also prioritize the actions necessary to establishing a road map to successful implementation. Through discussion and the careful planning of the process of successfully implementing Six Sigma, employees will have an easier journey to success in applying the methodology to their projects. Establishing a customer focus mind-set within an organization goes hand in hand with creating a successful Six Sigma business strategy. The factors that are critical to your customers’ success are necessary to a process improvement team’s true success. Therefore, evaluating customers’ perception of quality should be at the forefront of the implementation process. Every complaint from a customer should be viewed as an opportunity for growth and increased market share—a spotlight on areas needing process improvement focus. The key to success in this initial step is to make it easy for your customers’ comments to be heard. Various methods exist to obtain this valuable input, including: • Walking the customer process • Performing customer surveys • Conducting personal interviews with key customers • Establishing feedback/complaint systems • Developing customer panels



Depending on the size of your organization and its core values, the word “customer” can take on many different definitions. When collecting feedback, care should be taken to maintain a comprehensive view of your customers. By combining external feedback with such things as internal business strategies, employee needs, and government regulations, your organization will obtain a balanced list of customer needs. Through customer feedback, learning about what works and what does not will help to establish a mind-set of continual process improvement within your organization. Jack Welch, former CEO of GE and the most visible advocate of Six Sigma, has been quoted as saying that a business strategy alone will not generate higher quality throughout an organization.

Implementing Six Sigma As discussed previously, Six Sigma can be a great success or failure, depending on how it is implemented. Implementation strategies can vary significantly between organizations, depending on their distinct culture and strategic business goals. After completing a needs assessment and deciding to implement Six Sigma, an organization has two basic options: • Implement a Six Sigma program or initiative • Create a Six Sigma infrastructure

Part V.C

Option 1: Implement a Six Sigma Program or Initiative. The traditional approach to deploying statistical tools within an organization has not been very effective. With this approach, certain employees (practitioners) are taught the statistical tools from time to time and asked to apply a tool on the job when needed. The practitioners might then consult a statistician if they need help. Successes within an organization might occur; however, these successes do not build upon each other to encourage additional and better use of the tools and overall methodology. When organizations implement Six Sigma as a program or initiative, it often appears that they only have added, in an unstructured fashion, a few new tools to their toolbox through training classes. A possible extension of this approach is to apply the tools as needed to assigned projects. However, the selection, management, and execution of projects are not typically an integral part of the organization. These projects, which often are created at a low level within the organization, do not have the blessing of upper management; hence, resistance is often encountered when the best solution directly affects another group that does not have buy-in to the project. In addition, there typically is no one assigned to champion projects across organizational boundaries and facilitate change. A program or initiative does not usually create an infrastructure that leads to bottom-line benefits through projects tied to the strategic goals of the organization. As a program or initiative, Six Sigma risks becoming the “flavor of the month” and will not capture the buy-in necessary to reap a large return on the investment in training. With this approach, employees may end up viewing Six Sigma as a program similar to total quality management (TQM) and other quality programs that may have experienced only limited success within their organization. Even if great accomplishments occur through the individual use of statistical tools within organizations, there is often a lack of visibility of the benefits to



upper management. A typical missing element for success with this approach is management buy-in. Because of this lack of visibility, practitioners often have to fight for funds and may be eliminated whenever the times get rough financially. Effective use of statistical tools often does not get recognized and the overall company culture is not affected. For true success, executive-level support is needed that asks the right questions and leads to the wise application of statistical tools and other Six Sigma methodologies across organizational boundaries. Option 2: Create a Six Sigma Infrastructure. Instead of focusing on the individual tools, it is best when Six Sigma training provides a process-oriented approach that teaches practitioners a methodology to select the right tool, at the right time, for a predefined project. Training of Six Sigma practitioners (Black Belts) utilizing this approach typically consists of four weeks of training over four months, where students work on their projects during the three weeks between training sessions. Deploying Six Sigma as a business strategy through projects instead of tools is the more effective way to benefit from the time and money invested in Six Sigma training. Consider the following benefits of Six Sigma deployment via projects that have executive management support: • Offers bigger impact through projects tied to bottom-line results • Utilizes the tools in a more focused and productive way • Provides a process/strategy for project management that can be studied and improved • Increases communications between management and practitioners via project presentations • Facilitates the detailed understanding of critical business processes • Gives employees and management views of how statistical tools can be of significant value to organizations • Allows Black Belts to receive feedback on their project approach during training

Part V.C

• Deploys Six Sigma with a closed-loop approach, creating time for auditing and incorporating lessons learned into an overall business strategy A project-based approach relies heavily on a sound project selection process. Projects should be selected that meet the goals of an organization’s business strategy. Six Sigma can then be utilized as a road map to effectively meet those goals. Once strategic projects are selected, many practitioners (Black Belts) have found a “21-step integration of tools” road map helpful in developing a plan for specific projects. Initially, companies might have projects that are too large or perhaps are not chosen because of their strategic impact to the bottom line. Frustration with the first set of projects can be vital experience that motivates improvement in the second phase. Six Sigma is a long-term commitment. Treating deployment as a process allows objective analysis of all aspects of the process, including project selection and scoping. Utilizing lessons learned and incorporating them into subsequent waves of an implementation plan creates a closed feedback loop and real



opportunities for improvement. Deploying Six Sigma through projects can lead to dramatic bottom-line benefits if the organization invests the time and executive energy necessary to implement Six Sigma as a business strategy!

The Metrics of Six Sigma Much confusion exists relative to the metrics of Six Sigma. The sigma level (that is, sigma–quality level) sometimes used as a measurement within a Six Sigma program includes a ±1.5s value to account for “typical” shifts and drifts of the mean, where s is the standard deviation of the process. This sigma–quality level relationship is not linear. In other words, a percentage unit improvement in parts per million (ppm) defect rate (or defect per million opportunity [dpmo] rate) does not equate to the same percentage improvement in the sigma–quality level. Figure 29.2 shows the sigma–quality level associated with various services (considering the 1.5s shift of the mean). From this figure, we note that the sigma– quality level of most services is about four sigma, while “world class” is considered six. Figures 29.3, 29.4, and 29.5 illustrate various aspects of a normal distribution as it applies to Six Sigma program measures and the implication of the 1.5s shift. Figure 29.3 illustrates the basic measurement concept of Six Sigma, where parts are to be manufactured consistently and well within their specification range. Figure 29.4 shows the number of parts per million (ppm) that would be outside the specification limits if the data were centered within these limits and had various standard deviations. Figure 29.5 extends Figure 29.3 to noncentral data relative to specification limits, where the mean of the data is shifted by 1.5s. Figure 29.6 shows 1,000,000 100,000 10,000 ppm rate

Average company 1,000 100 10 Best in class 1

0.01 1

2

3

4

5

6

7

Part V.C

Domestic airline flight fatality rate (.43 ppm)

0.1

Sigma level (with ± 1.5 sigma shift)

Figure 29.2 Implication of sigma–quality level. Parts per million (ppm) rate for part or process step, considers a 1.5s shift of the mean where only 3.4 ppm fail to meet specifications at a six sigma quality level. Source: F. W. Breyfogle III, Implementing Six Sigma (John Wiley & Sons, 1994). Adapted by permission of John Wiley & Sons, Inc.



LSL

USL Specification range Customer needs

1350 ppm

1350 ppm

0.001 ppm

0.001 ppm ± 3s ± 6s

Figure 29.3

Normal distribution curve illustrates three sigma and six sigma parametric conformance.

Copyright of Motorola, used with permission.

LSL

USL Normal distribution centered

–6s

–3s

–2s

–1s

Part V.C

Specification limit ± 1 sigma ± 2 sigma ± 3 sigma ± 4 sigma ± 5 sigma ± 6 sigma

Figure 29.4

m

+1s

Percent 68.27 95.45 99.73 99.9937 99.999943 99.9999998

+2s

+3s

+6s

Defective ppm 317300 45500 2700 63 0.57 0.002

With a centered normal distribution between six sigma limits, only two devices per billion fail to meet the specification target.




LSL

USL Normal distribution shifted 1.5r

–6s

–3s

–2s

Specification limit ± 1 sigma ± 2 sigma ± 3 sigma ± 4 sigma ± 5 sigma ± 6 sigma

Figure 29.5

m

–1s

+1s

Percent 30.23 69.13 93.32 99.3790 99.97670 99.9996600

+2s

+3s

+6s

Defective ppm 697700 308700 66810 6210 233 3.4

World class

Effects of a 1.5s shift where only 3.4 ppm fail to meet specifications.


10× improvement 30× improvement

100,000 66,810 ppm Defect rate (ppm)

10,000 70× improvement

6210 ppm

1,000 100

233 ppm

10 3.4 ppm

1

Part V.C

1.5 sigma shift: Centered:

0.1 0.01 0.001 2

3

4

5

6

Sigma–quality level

Figure 29.6

Defect rates (ppm) versus sigma–quality level.

Source: F. W. Breyfogle III, Implementing Six Sigma (John Wiley & Sons, 1994). Adapted by permission of John Wiley & Sons, Inc.



the relationship of ppm defect rates versus sigma-quality level for a centered and 1.5s shifted process, along with a quantification for the amount of improvement needed to change a sigma level. Refer to Appendix E for additional Z-values. To achieve this basic goal of a Six Sigma program might then be to produce at least 99.99966 percent “quality” at the “process step” and part level within an assembly (that is, no more than 3.4 defects per million parts or process steps if the process mean were to shift by as much as 1.5s ). If, for example, there was on the average one defect for an assembly that contained 40 parts and four process steps, practitioners might consider that the assembly would be at a four sigma quality level from Figure 29.6, since the number of defects in parts per million is: (1 ÷ 160)(106) = 6250. Problems that can occur using the sigma–quality level metric include: • The improvement from 4.1 to 4.2 sigma–quality level is not the same as improvement from 5.1 to 5.2 sigma–quality level. • Determining the number of opportunities for any given process can be dramatically different between individuals. • A sigma–quality level metric can be deceiving. For example, one process might have a 50 percent defective unit rate and a sigma–quality level much greater than six, while another process might have a 0.01 percent defective unit rate and have a sigma–quality level much worse than six. To illustrate this, first consider the counting of opportunities for failure within a computer chip as junctions and “components.” The sigma–quality level metric for this situation typically leads to a very large number of opportunities for failure for a given computer chip; hence, a very high sigma–quality level is possible even when the defective rate per unit is high. Compare this situation to another situation where there are only a very small number of components or steps required for a process. The sigma–quality level metric for this situation typically leads to a very low number of opportunities for failure; hence, a very low sigma–quality level metric is possible even when the defective rate per unit is low.

Part V.C

• The sigma–quality level metric can only be determined when there are specifications. Service/transactional applications typically do not have specifications like manufacturing does. When a sigma–quality level is forced on a service/ transactional situation, this can lead to the fabrication of specifications and alterations of these “specifications” to “make the numbers look good.” Another Six Sigma metric that describes how well a process meets requirements is process capability. A six sigma–quality level process is said to translate to process capability index values for Cp and Cpk requirements of 2.0 and 1.5 respectively. Unfortunately, there is much confusion with these values, even though the following basic equations for these metrics are simple: USL − LSL 6s ⎛ USL − m m − LSL ⎞ , = min ⎜ ⎟ 3s ⎠ ⎝ 3s

Cp = C pk



where USL is the upper specification limit, LSL is the lower specification limit, and s is standard deviation. Computer programs often will not even give the same answer for a given set of data. Some programs consider the standard deviation to be short-term, while others consider standard deviation to be long-term. There are many ways to estimate standard deviation. Breyfogle (1999) describes eight different approaches. Process capability indices are discussed in Chapter 38. It is best not to force a sigma–quality metric on the various groups and/or projects within an organization. It is most important to use the right metric for any given situation. However, we believe that the sigma–quality level metric should be included, along with the other Six Sigma metrics, in all Six Sigma training. The positive, negative, and controversial aspects of each Six Sigma metric should be covered within the training so that organizations can more effectively communicate with their customers and suppliers. Often, customers and suppliers ask the wrong questions relative to Six Sigma and other metrics. When people understand the pluses and minuses of each metric, then they can work with their customers and/or suppliers to direct their efforts toward the best metric for a given situation, rather than reacting to issues that result from mandated metrics that make no sense. The training people receive in Six Sigma should lead them to the right metric for a given situation. As depicted in Figure 29.7, in addition to devising a business strategy, organizations wanting success with Six Sigma must be able to understand, select, and communicate Six Sigma metrics, including: sigma-quality levels, Cp, Cpk, Pp, and Ppk, rolled throughput yield (RTY*), defects per million opportunities (dpmo), cost of poor quality (COPQ), and “30,000-foot level” control charts.

* Technical note: Calculation of rolled throughput yield. Reworks within an operation comprise what is termed the “hidden factory.” Rolled throughput yield measurements can give visibility to process steps that have high defect rates and/or rework needs. One way to find rolled throughput yield is: First determine yield for all process operations. Multiply these process operation yields together. A cumulative throughput yield up through a process step can be determined by multiplying the yield of the current step by the yields of previous steps. Rolled throughput yield (YRT or RTY) can be calculated from the number of defects per unit (DPU) using the relationship: YRT = e − DPU

P (X = x) =

e−λ λ x e = x!

− np

(np)

x

x = 0,, 1, 2 , 3 . . .

x!

Part V.C

To understand this relationship, consider that the probability of observing exactly x events in the Poisson situation is given by the Poisson probability density function (PDF):

where e is a constant approximately equal to 2.71828, x is the number of occurrences, and l can equate to a sample size multiplied by the probability of occurrence (that is, np). It then follows that −D

YRT = P ( X = 0) = e − λ = e U = e − DPU , where D is defects, U is unit, and DPU is defects per unit.



Cp, Cpk, Pp, Ppk

Metrics

Sigma–quality level

Provides insight to business

dpmo

Provides insight to process

RTY

Leads to the ‘‘right’’ activity

COPQ

6σ

‘‘30,000-foot level’’ metrics

Strategy

Program

Driven from the top

Initiative

Selecting the right players

Business strategy

Figure 29.7

Effective project selection

Six Sigma metrics and implementation strategy.

Care must be taken that the training an organization receives in Six Sigma metrics is not “sugar coated” or avoided. In addition to the careful selection of metrics, Six Sigma training also should address the effective use of statistical methodologies, providing insight into how one can best determine what truly is causing a problem.

SUSTAINING AND COMMUNICATING CHANGE

Part V.C

Many companies attempt to improve products with numerous small changes or “tweaks” to their current processes; however, changes frequently are not documented and the associated results not reported. Substantial results are rarely obtained with this half-hearted method of change. When employees in this type of corporate culture hear of a new initiative such as Six Sigma, they wonder what will be different. In today’s constantly changing marketplace, companies that are able to embrace change in a focused and proactive manner are leaders in their field. Companies who not only master the technical side of Six Sigma but also overcome the cultural challenges associated with change can realize significant bottom-line benefits. Launching a Six Sigma business strategy is an excellent opportunity to assess current culture in an organization. Consider the following questions: • How has your company historically dealt with change initiatives? • Does your company often make changes that do not last? • How effective are your project teams? • Are you frequently focusing on the same problem? • How do your employees attack problems and conduct their daily work? • What is required within your company culture to make continual process improvement a lasting change?



• What will prevent your company from achieving success with Six Sigma? By evaluating the key cultural drivers and restraints to embracing Six Sigma, organizations can develop plans that enhance the key drivers and mitigate the critical restraints. A common key driver of sustaining Six Sigma change that is often overlooked is communication plans. Company leaders usually implement Six Sigma because they possess a clear vision of what their company can achieve. Frequently, however, they do not realize the power behind effectively communicating this vision throughout the corporation. Executives need to get everyone engaged and speaking the language of Six Sigma. A shared vision of how Six Sigma fits the strategic needs of the business should be created. A communication plan should be carefully considered and executed with enthusiasm. If successful, it will be your biggest ally in key stakeholder buy-in. Creating and implementing Six Sigma does not guarantee tangible benefits within an organization. However, when Six Sigma is implemented wisely as a business strategy accompanied by effective metrics, as illustrated in Figure 29.7, organizations can yield significant bottom-line benefits. Through the wise implementation of Six Sigma, the successes of individual projects can build upon each other, gaining the sustained attention of executive management and resulting in a corporate culture change from a reactive or firefighting environment to a learning organization.

THE DMAIC PROCESS DMAIC is a data-driven quality strategy used to improve processes. It is an integral part of a Six Sigma initiative, but in general can be implemented as a standalone quality improvement procedure or as part of other process improvement initiatives such as lean. DMAIC is an acronym for the five phases that make up the process. Briefly, the phases are • Define the problem, improvement activity, opportunity for improvement, the project goals, and customer (internal and external) requirements. • Measure process performance.

• Improve process performance by addressing and eliminating the root causes. • Control the improved process and future process performance.

Part V.C

• Analyze the process to determine root causes of variation, poor performance (defects).

The DMAIC process easily lends itself to the project approach to quality improvement encouraged and promoted by Juran. There are many tools used at each step of the process, most of which are described in this handbook. The reader is encouraged to consult additional resources for detailed discussion of the DMAIC process, Six Sigma quality initiatives, and the numerous tools used. See, for example, the ASQ Web site at www.asq.org, Britz and Emerling (2000), Hahn, Doganaksoy,



and Stanard (2001), Hoerl (2001), Hoerl and Snee (2002), Snee and Hoerl (2007), Montgomery (2009b), and the numerous references within these sources.

THEORY OF CONSTRAINTS Theory of constraints is a problem-solving methodology that focuses on the weakest link in a chain of processes. Usually the constraint is the process that is slowest. Flow rate through the system can not increase unless the rate at the constraint increases. Theory of constraints lists five steps to system improvement: 1. Identify. Find the process that limits the effectiveness of the system. If throughput is the concern then the constraint often will have work in process (WIP) awaiting action. 2. Exploit. Use kaizen or other methods to improve the rate of the constraining process. 3. Subordinate. Adjust (or subordinate) the rates of other processes in the chain to match that of the constraint. 4. Elevate. If the system rate needs further improvement, the constraint may require extensive revision (or elevation). This could mean investment in additional equipment or new technology. 5. Repeat. If these steps have improved the process to the point where it is no longer the constraint, the system rate can be further improved by repeating these steps with the new constraint. The strength of the theory of constraints is that it employs a systems approach, emphasizing that improvements to individual processes will not improve the rate of the system unless they improve the constraining process.

LEAN ENTERPRISE

Part V.C

Achieving what is known as a lean enterprise requires a change in attitudes, procedures, processes, and systems. It is necessary to “zoom out” and look at the flow of information, knowledge, and material throughout the organization. In any organization there are multiple paths through which products, documents, and ideas flow. The process of applying lean thinking to such a path can be divided into the following steps: 1. Produce a value stream map (VSM). This is also referred to as a value chain diagram. This diagram is described in detail by Rother and Shook (1999). It has boxes labeled with each step in the process. Information about timing and inventory is provided near each process box. Figure 29.8 shows an example of a value stream map. Some symbols that are used on value stream maps include: = inventory—originally a tombstone shape indicating dead material. = supermarket where employees can pick needed parts. Supermarkets are usually replenished by stockroom staff.



Daily orders

Production control

Orders and forecasts

Distribution center

Suppliers

Schedule for each shift Daily

Hourly

Shipping

Process A Coil inventory: 10 days

Cycle time: 55 sec Changeover: 23 min

Process B Inventory 1 shift

C/T: 18 sec C/O: 7 min

Inventory 1 shift

Process H C/T: 37 sec C/O: 14 min Inventory 1 shift

Process E Shaft inventory 5 days


Figure 29.8

Process G Inventory 1.5 shifts


Value stream map.

= Kanban post where cards or other visual signals are displayed.

315 sec.

3 sec.

7 sec. 275 sec.

= graph of value-added versus non-value-added times.

Part V.C

= visual signal used to make stocking decisions.

2. Analyze all inventory notations with an eye toward reduction or elimination. Inventory tends to increase costs because: • Storage space may be expensive (rubber awaiting use in a tire factory is stored at 120°F; wood inventory may need to have humidity control). • Quality may deteriorate (rust, spoilage, and so on)



• Design changes may be delayed as they work their way through the inventory. • Money invested in inventory could be used more productively elsewhere. • Quality problems that are not detected until a later stage in the process will be more expensive to correct if an inventory of defective products has accumulated. One company refers to its racks of safety stock as the “wall of shame.” 3. Analyze the entire value stream for unneeded steps. These steps are called non-value-added activities. 4. Determine how the flow is driven. Strive to move toward value streams in which production decisions are based on the pull of customer demand. In a process where pull-based flow has reached perfection, a customer order for an item would trigger the production of all the component parts for that item. These components would arrive, be assembled, and delivered in a time interval that would satisfy the customer. In many situations this ideal has not been reached and the customer order will be filled from finished goods inventory. The order will, however, trigger activities back through the value chain that produce a replacement part in finished goods inventory before it is needed by a customer. 5. Extend the value stream map upstream into suppliers’ plants. New challenges occur regarding compatibility of communication systems. The flow of information, material, knowledge, and money are all potential targets for lean improvements. When beginning the process, pick a narrow focus—do not try to boil the ocean as the saying goes.

Part V.C

Continuous Flow Manufacturing The traditional manufacturing strategy is to study the marketplace to obtain a forecast of sales of various products. This forecast is used as a basis for orders that are issued to suppliers and to departments responsible for fabrication and assembly. This is referred to as a push system. One major problem with this strategy is that if the forecast is imperfect, products are produced that are not wanted by customers and/or products that customers want are not available. A second major problem with the forecast-based strategy is the increasing expectation of customers for exactly the product they want exactly when they want it. These two problems have led to a response by manufacturers that is sometimes called mass customization. As illustrated by the automotive industry, a customer order of a vehicle with choices among dozens of options with perhaps hundreds of possible combinations can not be accurately forecast. Instead, the customer order initiates the authorization to build the product. This is referred to as a “pull” system because the pull of the customer instead of the push of the forecast activates the system.



Rather than producing batches of identical products, a pull-oriented organization produces a mix of products with the mix of features that customers order. In the ideal pull system, the receipt of the customer order initiates orders for the component parts to be delivered to the assembly line at scheduled times. The mixture of features of the components as they continuously flow to and through the line result in exactly the product the customer needs. Making this happen in a reasonable amount of time would have been unthinkable only a few years ago. When a pull system is in a state of perfection, each activity moves a component through the value stream so that it arrives at the next activity at the time it is needed. Achieving and maintaining this may require a great deal of flexibility in allocating resources to various activities. Cross-training of personnel is essential. The resulting flexibility and system nimbleness permits reduction of WIP.

Non-Value-Added Activities

Part V.C

Some functions perform activities that do not change the form or function of the product or service. The customer is not willing to pay for these activities. These activities are labeled non-value-added. A classic example is rework. The customer expects to pay for the printing of a document, for instance, but does not want to pay for corrections caused by supplier error. A key step in making an organization more lean is the detection and elimination of non-value-added activities. In searching for non-value-added activities the operative guideline should be “question everything.” Steps that are assumed to be necessary are often rife with opportunities for improvement. Team members not associated with a process will often provide a fresh eye and ask the impertinent questions. Some authors list seven or eight categories of waste or muda as it is referred to in some sources. These lists usually include overproduction, excess motion, waiting, inventory, excess movement of material, defect correction, excess processing, and lost creativity. The following paragraphs examine the causes and results of each of these wastes. Overproduction is defined as making more than is needed or making it earlier or faster than is needed by the next process. The principal symptom of overproduction is excess work in process (WIP). Companies adopt overproduction for various reasons including long setup times, unbalanced workload, and a just-in-case philosophy. One company maintains a six-month supply of a particular small part because the machine that produces it is unreliable. In some cases accounting methods have dictated that machines overproduce to amortize their capital costs. All WIP should be continuously scrutinized for possible reduction or elimination. Excess motion can be caused by poor workplace layout including awkward positioning of supplies and equipment. This results in ergonomic problems, time wasted searching for or moving supplies or equipment, and often in reduced quality levels. “Kaizen events” have been effectively used to focus a small short-term team on improvements in a particular work area. The team must include personnel with experience at the positions involved as well as those with similar functions elsewhere. In addition it is essential to include people with the authority to make decisions. Such teams have made startling changes in two to five days of intense activity.



Waiting typically is caused by such events as delayed shipments, long setup time, or missing people. This results in waste of resources and perhaps more importantly demoralization of personnel. Setup time reduction efforts and total productive maintenance are partial answers to this problem. Cross-training of personnel so that they can be effectively moved to other positions is also helpful in some cases. Most important, of course, is carefully laid and executed scheduling. Inventory is wasteful when inventories of raw materials, finished goods, or work in process are maintained, costs are incurred for environmental control, record keeping, storage and retrieval, and so on. These functions add no value to the customer. Of course some inventory may be necessary, but if a competitor finds ways to reduce costs by reducing inventory, business may be lost. One of the most tempting times to let inventory levels rise is when a business cycle is in the economic recovery phase. Instead of increasing inventories based on forecasts, the proper strategy is to synchronize production to increase with actual demand. Similarly, production or administrative functions that use more space or other resources than necessary increase costs without adding value. The overused analogy of the sea of inventory shown in Figure 29.9 illustrates how excess inven-

Inventory level

Order

Long setup times Unbalanced workload

Poor quality

Poor maintenance And so on

Part V.C

Inventory level

(a)

Order

Long setup times Unbalanced workload

Poor quality

Poor maintenance And so on

(b)

Figure 29.9

A sea of inventory often hides unresolved problems.



tory makes it possible to avoid solving other problems. As the level of inventory is lowered, some problems will rear their ugly heads and need to be solved before further progress is possible. Excess movement of material as indicated by large conveyor systems, huge fleets of fork lifts, and so on, makes production more costly and complex, often reducing quality through handling and storing. Poor plant layout is usually to blame. Plants with function-oriented departments (such as all lathes together and all presses together) require excessive material movement. A better plan is to gather equipment together that is used for one product or product family. This may mean having a manufacturing cell contain several types of equipment requiring personnel with multiple skills. Many companies have had success with cells that form a C shape as shown in Figure 29.10 because they can be staffed in several ways. If demand for the cell’s output is high, six people could be assigned there, one per machine. If demand is very low, one person could move from machine to machine producing parts one at a time. Defect correction is non-value-added because the effort required to fix the defective part is wasted. Typical causes of defects are poor equipment maintenance, poor quality system, poor training and/or work instructions, and poor product design. Lean thinking demands a vigorous look at these and other causes in order to continuously reduce defect levels. Excess processing is often difficult to recognize. Sometimes entire steps in the value chain are non-value-added. A steel stamping operation produces a large volume of parts before they are scheduled for painting, which may cause the practice of dipping parts in an oil solution to prevent rust as they wait to be painted. As the paint schedule permits, the parts are degreased and painted. The customer is unwilling to pay for the dip/degrease activities because they do not enhance the product. The best solution in this case is to schedule the pre-paint activities so that the parts are painted immediately upon production. This solution may require smaller batch sizes and improved communication procedures, among

Machine #2

Material flow

Part V.C

Figure 29.10

Machine #5

Machine #4

Machine #6

Machine #3

Machine #1

C-shaped manufacturing cell.



Part V.C

other things. The purpose of the grinding step that often follows a welding operation is to remove some of the weld imperfections. Improving the welding process may reduce or eliminate the need for grinding. In this case, the unnecessary grinding would be classified as excessive processing. Excessive processing can occur in the office as well as on the plant floor. Information from customer purchase orders (POs) is sometimes entered into a database and the order itself is filed as a backup hard copy to resolve any later disagreements. A recent study by one company revealed the fact that the hard copies—although they are occasionally pulled from files and initialed, stamped, stapled, and so on—really serve no useful purpose. The company now discards the PO once the information has been entered. The processes of filing, storing, and maintaining these records required one-half person performing non-value-added activity. Lost creativity is perhaps the most unfortunate waste. Most manufacturing employees have ideas that would improve processes if implemented. Standard organizational structures sometimes seem designed to suppress such ideas. Union/management divides seem almost impossible to bridge. Lean thinking recognizes the need to involve employees in teams that welcome and reward their input. These teams must be empowered to make changes in an atmosphere that accepts mistakes as learning experiences. The resulting improved morale and reduced personnel turnover impact the bottom line in ways that no accountant has calculated. There are, of course, gray areas where the line between valued-added and non-value-added may not be obvious. One such area is inspection and testing. A process may be so incapable that its output needs to be inspected to prevent defective parts from entering downstream processes. It could be argued that this inspection is a value-added activity because the customer does not want defective products. The obvious solution is to work on the process, making it capable and rendering the inspection activity unnecessary. Most authorities would agree that this inspection is non-value-added. On the other hand, a gas furnace manufacturer must fire test every furnace in order to comply with Canadian Standards Association (CSA) requirements. Customers are willing to pay for the CSA listing, so this test step is a value-added activity. Studies have shown that an overwhelming percent of lead time is non-valueadded, much of it spent waiting for the next step. Yet over the years efforts to decrease lead time often have focused on accelerating value-added functions rather than reducing or eliminating non-value-added functions.

Cycle Time Reduction Another aspect of the value chain that should be studied is the cycle time. Cycle time is displayed below each process in Figure 29.8. It is defined as the amount of time required to complete the named activity for one product or service. If the cycle time is variable it is useful to show a range and average on the value stream map. Reducing variation in cycle time makes a system more predictable. Sometimes the cycle time variation can be reduced by using the cycle times of sub-activities instead. For example, suppose the activity consists of using a word processor to modify a standard bid form. Sub-activities might include inserting



client information, listing proposed budget, detailing alternatives, and so on. The total time to prepare the bid might vary a great deal while the time required to accomplish each sub-activity should show less variation. The activities performed should be continuously studied in an effort to eliminate non-value-added components and find better and faster ways to complete the value-added components. Techniques that have been successfully applied to accomplish these goals are variously called kaizen methods, kaizen blitz, rapid continuous improvement (RCI), and similar names. The usual procedure is to form a small team that is given a process to improve and a limited time frame, often only a few days. The team should include the people who perform the targeted activity, outsiders who can provide a fresh perspective, as well as people who are authorized to approve changes. The team observes the process and raises questions about its various parts. Typical questions might include: • Why is that stored there? Is there a better place to put it? • Why do things in that order? • Would a different table height work better? • Could your supplier (internal or external) provide a better service? Does your supplier know what you need? • Are you providing your customer, whether internal or external, with the best possible services? • Do you know what your customer needs? • Should parts of this activity be performed by the customer or the supplier? • Are there steps that can be eliminated? • Is there enough light, fresh air, and so on, to do the job efficiently? • Would another tool, software package, or other material be more helpful? • Are tools conveniently and consistently stored? • Can the distance the person and/or product moves be reduced? • Should this activity be moved closer to the supplier or customer? • How many of these items should be kept on hand?

In other words, the team questions everything about the process and its environment. Kaizen activity usually results in making several small improvements. In many situations the team actually implements a change and studies the result before making a recommendation. Cycle time must not be confused with takt time. Takt time is determined by customer demand. Its formula is

Part V.C

• Would it help to do this activity in less space?

Takt time = (time available) ÷ (units required)



For example if 284 units are to be produced in a shift consisting of 27,000 seconds, then Takt time = 27,000 ÷ 284 ≅ 95 seconds. That is, the system must average one unit every 95 seconds. To meet this demand rate, the cycle time for each process must be less than 95 seconds. So the basic relationship between cycle time and takt time is Cycle time < takt time If cycle time exceeds takt time, more than one person is needed. To approximate the number of people required, use the following formula: Number of people required =

cycle time takt time

Takt time is recalculated whenever the production schedule is changed. In the example, if 312 units are scheduled, the takt time is reduced to 87 seconds. Adjustments to cycle times, possible by adding people or equipment, may be necessary.

Part V.C

Lean Tools In a visual factory, locations for tools, inventory, safety equipment, and so on, are clearly marked and identified. Signs and floor paint designate traffic patterns and storage locations. Information needed by personnel to perform their functions is readily available. Monitors display current information about the activity. A kanban system may be used to simplify and improve resupply procedures. In a typical two-bin kanban arrangement, as the first bin is emptied, the user signals roving resupply personnel. The signal is usually visual and may be placement of a card that came with the bin, turning on a light, or just displaying the empty bin. The resupply employee gathers the information on supplies needed and replenishes the bins. Sometimes the bins are resupplied from a stockroom although often it is from a closer supply point sometimes referred to as the supermarket. In some cases, bins are replenished directly by an outside vendor. The entire string of events occurs routinely, often with no paperwork. The result is smoother flow and less inventory. A poka-yoke device is designed to prevent errors. Suppose several people place documents in four separate trays depending on document type. A kaizen team discovered that a person sorts the trays at the end of the day because about five percent of the documents are in the wrong tray even though signs clearly state document type. The team recommended printing the documents on different colored paper and also printing the signs on the corresponding paper color. This reduced the number of misplaced documents to 0.7 percent and made the sorting job much easier. Figure 29.11 illustrates a poka-yoke device used to ensure that round and square tubing items are placed in the correct containers. Poka-yoke methods are helpful in reducing the occurrence of rare events. A manufacturer finds that about one in 2000 of its assemblies shipped is missing one



Figure 29.11

A poka-yoke technique ensures that the round and square parts are placed in correct containers. Neither part will fit through the hole in the top of the incorrect container.

Part V.C

of its 165 components. A poka-yoke technique was used to eliminate this defect. The manufacturer now bar codes each component and scans serial number and component bar codes as each component is added to the assembly. The software is written so that the printer at the shipping department will not print a shipping label if any component is missing. Another poka-yoke example involves the selection of the correct part from several bins with similar contents. As the product reaches the workstation, its bar code is read. Light beams crisscross the front of the bins. If the operator reaches into the wrong bin, as determined from the bar code, the conveyor stops until appropriate corrections have been made. The lean tool called standard work states that each activity should be performed the same way every time. The application of this principle can help reduce variation in cycle time and produce a better, more consistent product or service and can also simplify downstream activities. The best procedure is to have the people involved with the activity reach a consensus regarding the standard method and agree to use it. The agreed-upon method should be documented and easily available to all involved. Charts and posters in the work area often are used to reinforce the method. These documents must be updated as continuous improvements are made. Lean thinking is built on timely satisfaction of customer demand, which means there must be a system for quickly responding to changes in customer requirements. In metal-forming industries it was common practice to produce thousands of parts of a particular type before changing the machine’s dies and producing thousands of another part. This often produced vast inventories of work in process and the associated waste. These procedures were justified because changing machine dies took several hours. The time required to change over from one part to another is displayed on the value chain map below each process. The system used to reduce changeover time and improve timely response to demand is called single minute exchange of dies (SMED). Shigeo Shingo is given credit for developing the SMED concept and using it in the Toyota Production System. The goal is to reduce the time from the last good part of one type to the first good part of the successive run. The initial application of SMED often requires considerable resources in special staging tables and die storage areas, among others. Activities done while the machine is down are referred to as internal activities versus the external activities



EXAMPLE 29.1: APPLICATION OF SMED TO A PHOTOGRAPHY OPERATION Formerly the procedure for changing cameras required several steps: 1. Shoot last good picture with camera A. 2. Remove camera A and its power supply and place in storage cupboard. 3. Remove type A tripod. 4. Remove type A lighting and reflectors. 5. Install type B lighting and reflectors. 6. Install type B tripod. Measure distance to subject with tape measure. 7. Locate camera B in cupboard and install it and its power supply. 8. Shoot first good picture with camera B.

Part V.C

A team working to reduce changeover time designed a fitting so both cameras could use the same tripod. Purchasing extra cables made it possible to avoid moving power supplies. More flexible lighting reflectors were designed so one set would work with all cameras. Taped marks on the floor now show where to locate tripod feet to avoid the necessity of using a tape measure. Another alternative would be to obtain a more versatile camera that would not need to be changed.

performed in preparation for or follow-up to the die change. Shingo’s method is to move as many activities from internal to external as possible. A useful technique is to make a video recording of a typical changeover and have involved personnel use it to identify internal activities that can be converted to external activities. Positioning correct tooling, equipment, and manpower should all be done in external time. Activities that don’t involve die changes also need to be nimble in their response to changing customer requirements. This can be achieved through analysis of the changeover process. An assembly department that produced three different models spent considerable time converting the assembly line from one model to another. They found that three different assembly lines worked best for them. They now switch models by walking across the room. Opportunities to apply SMED concepts abound in many businesses and industries. Recognizing and developing these opportunities depend on the creativity and perseverance of the people involved.

Total Productive Maintenance In order for lean systems to work, all equipment must be ready to quickly respond to customer needs. This requires a system that foresees maintenance needs and takes appropriate action. A total productive maintenance (TPM) system uses



historical data, manufacturer’s recommendations, reports by alert operators, diagnostic tests, and other techniques to schedule maintenance activity so that machine downtime can be minimized. Total productive maintenance goes beyond keeping everything running, however. A TPM system includes continuous improvement initiatives as it seeks more effective and efficient ways to predict and diagnose problems.

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Chapter 30 D. Corrective Action

Identify, describe, and apply elements of the corrective action process including problem identification, failure analysis, root cause analysis, problem correction, recurrence control, verification of effectiveness, etc. (Evaluate) Body of Knowledge V.D

Corrective and preventive actions often are best taken using a problem-solving method. Problem-solving methods (also called the scientific method) have many variations depending, to some extent, on the use; however, they are all similar. The seven phases of corrective action are shown in Figure 30.1, which also shows the relationship to the PDSA cycle. The phases are integrated in that they are all dependent on the previous phase. Continuous improvement is the objective and these phases are the framework to achieving that objective.

Part V.D

Phase 1 Identify the opportunity

Phase 7 Plan for the future

Phase 6 Standardize the solution

Act

Plan

Study

Do

Phase 5 Study the results

Figure 30.1

Phase 2 Analyze the process

Phase 3 Develop the optimal solution(s)

Phase 4 Implement

The seven phases of corrective action.

346


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 30: D. Corrective Action 347

PHASE 1: IDENTIFY THE OPPORTUNITY (PROBLEM IDENTIFICATION) The objective of this phase is to identify and prioritize opportunities for improvement. It consists of three parts: identify the problem, form the team (if one is not in existence), and define the scope. Problem identification answers the question, “What are the problems?” The answer leads to those problems that have the greatest potential for improvement and have the greatest need for solution. Problems can be identified from a variety of inputs, such as: • Pareto analysis of repetitive external alarm signals, such as field failures, complaints, returns, and others • Pareto analysis of repetitive internal alarm signals (for example, scrap, rework, sorting, and the 100 percent test) • Proposals from key insiders (managers, supervisors, professionals, and union stewards) • Proposals from suggestion schemes • Field study of users’ needs • Data on performance of competitors (from users and from laboratory tests) • Comments of key people outside the organization (customers, suppliers, journalists, and critics) • Findings and comments of government regulators and independent laboratories • Customer surveys • Employee surveys • Brainstorming by work groups Problems identified provide opportunities for improvement. For a condition to qualify as a problem, it must meet the following three criteria: 1. Variable performance from an established standard

3. The cause is unknown; if we know the cause, there is no problem Identifying problems for improvement is not difficult, as there often are many more than can be analyzed. The quality council or work group must prioritize them using the following selection criteria:

Part V.D

2. Deviation from the perception and the facts

1. Is the problem important and not superficial? Why? 2. Will problem solution contribute to the attainment of goals? 3. Can the problem be defined clearly using objective measures?



In selecting its initial improvement opportunity, a work group should find one that gives the maximum benefit for the minimum amount of effort. The second part of phase 1 is to form a team. If the team is a natural work group or one where members already work together, then this part is complete. If the problem is of a multifunctional nature, as most are, then the team should be selected and directed by the quality council to address the improvement of a specific process. The team leader is then selected and becomes the owner of the process improvement. Goals and milestones are established. If the improvement strategy is the repair or refinement of an existing process, an individual, rather than a team, may be assigned. The third part of phase 1 is to define the scope. Failure in problem solving is frequently caused by poor definition of the problem. A problem well stated is half solved. Criteria for a good problem statement are as follows: • It clearly describes the problem as it currently exists and is easily understood • It states the effect—what is wrong, when it happens, and where it is occurring, not why it is wrong or who is responsible • It focuses on what is known, what is unknown, and what needs to be done • It uses facts and is free of judgment • It emphasizes the impact on the customer An example of a well-written problem statement is: As a result of a customer satisfaction survey, a sample of 150 billing invoices showed that 18 had errors that required one hour to correct. This example statement describes the current state. We might also wish to describe the desired state, such as “Reduce billing errors by 75 percent.” In addition to the problem statement, this phase requires a comprehensive charter for the team. The charter specifies: 1. Authority. Who authorized the team?

Part V.D

2. Objective and scope. What are the expected outputs and specific areas to be improved? 3. Composition. Who are the team members and process and subprocess owners? 4. Direction and control. What are the guidelines for the internal operation of the team? 5. General. What are the methods to be used, the resources, and the specific milestones?



PHASE 2: ANALYZE THE CURRENT PROCESS The objective of this phase is to understand the process and how it is currently performed. Key activities are to define process boundaries, outputs and customers, inputs and suppliers, and process flow, determine levels of customer satisfaction and measurements needed, gather data, and identify root causes. The first step is for the team to develop a process flow diagram. A flow diagram translates complex work into an easily understood graphic description. This activity often is an eye-opening experience for the team, because it is rare that all members of the team understand the entire process. Next, the target performance measures are defined. Measurement is fundamental to meaningful process improvements. If something cannot be measured, it cannot be improved. There is an old saying that what gets measured gets done. The team will determine if the measurements needed to understand and improve the process are presently being used; if new ones are needed, they will: • Establish performance measures with respect to customer requirements • Determine data needed to manage the process • Establish regular feedback with customers and suppliers • Establish measures for quality/cost/timelines of inputs and outputs Once the target performance measures are established, the team can collect all available data and information. If these data are not enough, then additional new information is obtained. Gathering data: (1) helps confirm that a problem exists, (2) enables the team to work with facts, (3) makes it possible to establish measurement criteria for a baseline, and (4) enables the team to measure the effectiveness of an implemented solution. It is important to collect only needed data and to get the right data for the problem. The team should develop a plan that includes input from internal and external customers and ensures that the plan answers the following questions: 1. What problem or operation do we wish to learn about? 2. What are the data used for? 3. How much data is needed?

5. What action should be taken as a result of the conclusion? Data can be collected by a number of different methods, such as check sheets, computers with application software, data-collection devices like hand-held gages, or an online system. The team will identify the customers and their requirements and expectations as well as the inputs, outputs, and interfaces of the process. Also, they will systematically review the procedures currently being used. Common items of data and information are:

Part V.D

4. What conclusions can be drawn from the collected data?



• Customer information, such as complaints and surveys • Design information, such as specifications, drawings, function, bills of materials, costs, design reviews, field data, service, and maintainability • Process information, such as routing, equipment, operators, raw material, and component parts and supplies • Statistical information, such as average, median, range, standard deviation, skewness, kurtosis, and frequency distribution • Quality information, such as Pareto diagrams, cause-and-effect diagrams, check sheets, scatter diagrams, control charts, histograms, process capability, acceptance sampling, run charts, life testing, inspection steps, and operator and equipment matrix analysis • Supplier information, such as process variation, on-time delivery, and technical competency The cause-and-effect diagram is particularly effective in this phase. Determining all of the causes requires experience, brainstorming, and a thorough knowledge of the process. It is an excellent starting point for the project team. One word of caution—the object is to seek causes, not solutions. Therefore, only possible causes, no matter how trivial, should be listed. It is important to identify the root cause. This activity can sometimes be determined by voting. It is a good idea to verify the most likely cause because a mistake here can lead to the unnecessary waste of time and money by investigating possible solutions to the wrong cause. Some verification techniques are: 1. Examine the most likely cause in regard to the problem statement 2. Recheck all data that support the most likely cause 3. Check the process when it is performing satisfactorily versus when it is not by using the who, where, when, how, what, and why approach 4. Utilize an outside authority who plays “devil’s advocate” with the data, information, and reasoning

Part V.D

5. Use experimental design, Taguchi’s quality engineering, and other advanced techniques to determine the critical factors and their levels 6. Save a portion of the data used in the analysis to confirm during verification Once the root cause is determined, the next phase can begin.

PHASE 3: DEVELOP THE OPTIMAL SOLUTION(S) (CORRECTION) The objectives of this phase are establishing potential and feasible solutions and recommending the best solution to improve the process. Once all the information is available, the project team begins its search for possible solutions. Frequently,



more than one solution is required to remedy a situation. Sometimes the solutions are quite evident from a cursory analysis of the data. In this phase, creativity plays the major role and brainstorming is the principal technique. Brainstorming on possible solutions requires not only a knowledge of the problem but innovation and creativity. There are three types of creativity: (1) create new processes, (2) combine different processes, or (3) modify the existing process. The first type is innovation in its highest form, such as the invention of the transistor. Combining two or more processes is a synthesis activity to create a better process. It is a unique combination of what already exists. This type of creativity relies heavily on benchmarking. Modification involves altering a process that already exists so that it does a better job. It succeeds when managers utilize the experience, education, and energy of empowered work groups or project teams. There is not a distinct line between the three types—they overlap (Rother and Shook 1999). Creativity is the unique quality that separates mankind from the rest of the animal kingdom. Most of the problems that cause inefficiency and ineffectiveness in organizations are simple problems. There is a vast pool of creative potential available to solve these problems. Quality is greatly improved because of the finding and fixing of a large number of problems, and morale is greatly increased because it is enormously satisfying to be allowed to create (Mallette 1993). Areas for possible change include the number and length of delays, bottlenecks, equipment, timing and number of inspections, rework, cycle time, and materials handling. Consideration should be given to simultaneously combining, eliminating, rearranging, and executing the process steps. Once possible solutions have been determined, evaluation or testing of the solutions comes next. As mentioned, more than one solution can contribute to the situation. Evaluation and/or testing determines which of the possible solutions has the greatest potential for success and the advantages and disadvantages of these solutions. Criteria for judging the possible solutions include such things as cost, feasibility, effect, resistance to change, consequences, and training. Solutions also may be categorized as short range and long range. At a minimum, the solution must prevent reoccurrence. Control charts give us the ability to evaluate possible solutions. Whether the idea is good, poor, or has no effect is evident from the chart.

PHASE 4: IMPLEMENT CHANGES Part V.D

Once the best solution is selected, it can be implemented. The objectives of this phase are preparing the implementation plan, obtaining approval, and implementing the process improvements. Although the project team usually has some authority to institute remedial action, more often than not the approval of the quality council or other appropriate authority is required. If such is the case, a written and/or oral report is given. The contents of the implementation plan report must fully describe: • Why it will be done • How it will be done



• When it will be done • Who will do it • Where it will be done The report will designate required actions, assign responsibility, and establish implementation milestones. The length of the report is determined by the complexity of the change. Simple changes may require only an oral report, whereas others require a detailed written report. After approval by the quality council, it is desirable to obtain the advice and consent of departments, functional areas, teams, and individuals that may be affected by the change. A presentation to these groups will help gain support from those involved in the process and provide an opportunity for feedback with improvement suggestions. The final element of the implementation plan is the monitoring activity that answers the following: • What information will be monitored or observed and what resources are required? • Who will be responsible for taking the measurements? • Where will the measurements be taken? • How will the measurements be taken? • When will the measurements be taken? Measurement tools such as run charts, control charts, Pareto diagrams, histograms, check sheets, and questionnaires are used to monitor and evaluate the process change.

Part V.D

PHASE 5: STUDY THE RESULTS The objective of this phase is monitoring and evaluating the change by tracking and studying the effectiveness of the improvement efforts through data collection and review of progress. It is vital to institutionalize meaningful change and ensure ongoing measurement and evaluation efforts to achieve continuous improvement. The team should meet periodically during this phase to evaluate the results to see that the problem has been solved or if fine-tuning is required. In addition, they will wish to see if any unforeseen problems have developed as a result of the changes. If the team is not satisfied, then some of the phases will need to be repeated.

PHASE 6: STANDARDIZE THE SOLUTION (RECURRENCE CONTROL) Once the team is satisfied with the change, it must be institutionalized by positive control of the process, process certification, and operator certification. Positrol



(positive control) assures that important variables are kept under control. It specifies the what, who, how, where, and when of the process and is an updating of the monitoring activity. Standardizing the solution prevents backsliding. Table 30.1 gives an illustration of a few variables of a wave soldering process. In addition, the quality peripherals—the system, environment, and supervision—must be certified. The partial checklist in Table 30.2 provides the means to initially evaluate the peripherals and periodically audit them to ensure that the process will meet or exceed customer requirements for the product or service. Finally, operators must be certified to know what to do and how to do it for a particular process. Also needed is cross-training in other jobs within the process to ensure next-customer knowledge and job rotation. Total product knowledge is also desirable. Operator certification is an ongoing process that must occur periodically.

Table 30.1

Positrol of a wave soldering process.

What

Specs

Who

How

Where

When

An 880 flux

0.864 g ± 0.0008

Lab technician

Specific gravity meter

Lab

Daily

Belt speed

ft/min ± 10%

Process technician

Counter

Board feed

Each change

Preheat temperature

220˚ ± 5˚

Automatic

Thermocouple

Chamber entrance

Continuous

Reprinted from World Class Quality by Kiki Bhote. ©1991 AMACOM, a division of the American Management Association International. Reprinted by permission of AMACOM, a division of American Management Association International, New York, NY. All rights reserved. http://www.amanet.org

Table 30.2 Checklist for process certification. Environment

Supervision

Authority to shut down line

Water/air purity

Coach, not boss

Preventive maintenance

Dust/chemical control

Clear instructions

Visible, audible alarm signals

Temperature/humidity control

Combining tasks

Foolproof inspection

Electrostatic discharge

Encourage suggestions

Neighbor and self-inspection

Storage/inventory control

Feedback of results

Part V.D

Quality system

Reprinted from World Class Quality by Kiki Bhote. ©1991 AMACOM, a division of the American Management Association International. Reprinted by permission of AMACOM, a division of American Management Association International, New York, NY. All rights reserved. http://www.amanet.org



PHASE 7: PLAN FOR THE FUTURE (EFFECTIVENESS ASSESSMENT)

Part V.D

This phase has the objective of achieving improved levels of process performance. Regardless of how successful initial improvement efforts are, the improvement process must continue. Everyone in the organization is involved in a systematic long-term endeavor to constantly improve quality by developing processes that are customer-oriented, flexible, and responsive. A key activity is to conduct regularly scheduled reviews of progress by the quality council and/or work group. Management must establish the systems to identify areas for future improvement and to track performance with respect to internal and external customers. They also must track changing customer requirements. Continuous improvement means not being satisfied with merely doing a good job or process but striving to improve that job or process. It is accomplished by incorporating process measurement and team problem solving in all work activities. TQM tools and techniques are used to improve quality, delivery, and cost. We must continuously strive for excellence by reducing complexity, variation, and out-of-control processes. Lessons learned in problem solving, communications, and group dynamics, as well as technical know-how, must be transferred to appropriate activities within the organization.



Chapter 31 E. Preventive Action

Identify, describe, and apply various preventive action tools such as errorproofing/poka-yoke, robust design, etc., and analyze their effectiveness. (Evaluate) Body of Knowledge V.E

The problem-solving method discussed in the previous chapter often may be useful for preventive actions. The function of quality engineers has moved from that of detection of defects to prevention. The concept of preventive action has been around for many years and has been practiced extensively in Japan, where it has the name poka-yoke. There are five categories of preventive systems: fail-safe devices, magnification of senses, redundancy, countdown, and special checking and control devices. Many kinds of fail-safe devices are used to ensure that problems or abnormalities in processes will be discovered in a manner that will maintain a safe working environment and ensure that quality is not compromised. See Table 31.1. Magnification of senses is used to increase the power of human seeing, hearing, smelling, feeling, tasting, and muscle power. Some examples are optical magnification, multiple visual and audio signals, remote-controlled viewing of a

Types of fail-safe devices.

Type of fail-safe device

Device function

Interlocking sequences

Ensure that the next operation can not start until the previous operation is successfully completed

Alarms and cutoffs

Activate if there are any abnormalities in the process

All-clear signals

Activate when all remedial steps have been taken

Foolproof work-holding devices

Ensure that a part can be located in only one position

Limiting mechanisms

Ensure that a tool cannot exceed a certain position or amount

Part V.E

Table 31.1

355



hazardous process, robotic placement of parts or tools, and use of pictures rather than words. Redundancy is the use of additional activities as a quality safeguard. Multipleidentity codes, such as bar and color codes, are used to prevent product mix-ups. Redundant actions and approvals require two individuals working independently. Audit review and checking procedures assure that plans are being followed. Design for verification utilizes special designs, such as holes for viewing, to determine if the product or process is performing satisfactorily. Multiple test stations may check a number of attributes, such as those that occur on a highspeed production line. Another category is countdown, which structures sensing and information procedures to parallel the operating procedures in order to check each step. The most familiar example of this category of error-proofing is the launching of a space vehicle. It also has been effectively used in surgical operations and in welding. The last category is special checking and control devices. A familiar example is the computer checking of credit card numbers whereby invalid numbers are rejected and instant feedback provided. There are five error-proofing principles: elimination, replacement, facilitation, detection, and mitigation. Elimination of the possible error occurs when the process or product is redesigned so that the error can no longer occur. Replacement is a change to a more reliable process. Facilitation occurs when the process is made easier to perform and, therefore, more reliable. Detection occurs when the error is found before the next operation. Mitigation minimizes the effect of the error.

VERIFYING THE EFFECTIVENESS OF PREVENTIVE ACTIONS It is not enough merely to plan and execute preventive actions. In accordance with the PDSA and PDCA models, the effectiveness of preventive actions must be verified. Such verification can be difficult, however, since preventive actions that are effective eliminate problems before they occur. In this respect, verification of preventive actions is completed by ensuring that problems for which preventive actions have been planned and executed have not, in fact, recurred.

ROBUST DESIGN

Part V.E

A product or process is called robust if its function is relatively unaffected by variation in the environment in which it operates.

EXAMPLE 31.1 A laundry appliance must operate correctly with a variety of water chemistry and cleaning products as well as variations in temperature, humidity and other factors.


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EXAMPLE 31.2 An automobile must function correctly under various weather conditions as well as variation in operator techniques.

EXAMPLE 31.3 The process of assembling a bid must be performed in an area where frequent interruptions, phone calls, and so on, occur. In order to make certain that all 23 required elements of the bid document are included, 23 color-coded trays are set out with the appropriate form in each tray before the document is assembled.

EXAMPLE 31.4 The raw material for a punching process has a wide variation in thickness, in other words the process operates in an environment of thickness variation. This results in unacceptable burrs on some parts. One solution is to impose a tighter thickness specification on the raw material supplier. The robust design solution might be a new die that would prevent burrs regardless of the thickness.

SUMMARY OF PART V Part V described a broad spectrum of tools that have proved helpful in continuous improvement efforts. The novice practitioner may feel overwhelmed by the array of options. The best advice is, rather than study the tools and memorize their individual traits, to identify a problem and try to use one or more of the tools in its solution. Experience with these tools provides a depth of understanding never attainable from the written word alone.

Part V.E Licensed to BS Grupo SAC/null null by ASQ (www.asq.org) Order# 1001301680 Downloaded: 11/9/2009




Part VI Chapter 32 Chapter 33 Chapter 34 Chapter 35 Chapter 36 Chapter 37 Chapter 38 Chapter 39

A. B. C. D. E. F. G.

Collecting and Summarizing Data Quantitative Concepts Probability Distributions Statistical Decision Making Relationships Between Variables Statistical Process Control (SPC) Process and Performance Capability H. Design and Analysis of Experiments

Part VI

Quantitative Methods and Tools

This part covers eight topics in data analysis that CQEs must understand and routinely employ: collecting and summarizing data, quantitative concepts, probability distributions, statistical decision making, relationships between variables, statistical process control, process and performance capability, and the design and analysis of experiments.

359



Part VI.A.1

Chapter 32 A. Collecting and Summarizing Data

T

his chapter covers seven aspects related to collecting and summarizing data: types of data, measurement scales, data collection methods, data accuracy, descriptive statistics, graphical methods for depicting relationships, and graphical methods for depicting distributions.

1. TYPES OF DATA

Types of Data: Define, classify, and compare discrete (attributes) and continuous (variables) data. (Apply) Body of Knowledge VI.A.1

There are two types of data encountered in practice: discrete data and continuous data. Discrete (count) data are obtained when the characteristic being studied can only take on certain values and is countable. For example, number of nonconforming units in a lot, pass/fail data, or number of successes per trial. Another example would be the number of scratches on an object. In this case the possible values are 0, 1, 2, . . ., a so-called countably infinite set. In quality control, discrete data are referred to as attribute data. Continuous (variables) data are obtained when the characteristic being studied can take on any value over an interval of numbers. For example, the length of a part can be any value above zero. Between each two values on a continuous scale there are infinitely many other values. For example, between 2.350 inches and 2.351 inches the values 2.3502 inches, 2.35078 inches, and so on, occur.

360


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2. MEASUREMENT SCALES

Body of Knowledge VI.A.2

Part VI.A.3

Define, describe, and use nominal, ordinal, interval, and ratio scales. (Apply)

There are four types of measurement scales: nominal, ordinal, interval, and ratio. Nominal scales classify data into categories with no order implied, such as an equipment list of presses, drills, and so on. Sometimes we assign zero and one to represent, say, a conforming or nonconforming item; however, the numbers have no meaning in terms of order. Ordinal scale refers to positions in a series where order is important, but precise differences between values are not defined. For example, on the Mohs hardness scale of 10 minerals, talc has a hardness of one, fluorite has a hardness of four, and topaz has a hardness of eight. However, topaz is harder than fluorite, but not twice as hard. Another example might be survey responses such as strongly dissatisfied, dissatisfied, neutral, satisfied, and strongly satisfied, which can be scaled as 1, 2, 3, 4, 5. Interval scales have meaningful differences but no absolute zero. In this case, ratios are not meaningful. An example is temperature measured in degrees Fahrenheit (F). In this case, 20 degrees F is not twice as warm as 10 degrees F. Although the Fahrenheit scale has a zero, it is not an absolute zero. That is, the zero value does not signify that there is an absence of temperature. Data on an interval scale can be added and subtracted but can not be multiplied or divided. Ratio scales have meaningful differences and an absolute zero exists. One example of a ratio scale is length in inches because zero length is defined as having no length, and 20 inches is twice as long as 10 inches. Heat in degrees Kelvin (K) is another example of a ratio scale because zero degrees K is defined as having no heat and 10 degrees K has twice as much heat as five degrees K.

3. DATA COLLECTION METHODS

Describe various methods for collecting data, including tally or check sheets, data coding, automatic gaging, etc., and identify their strengths and weaknesses. (Apply) Body of Knowledge VI.A.3


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 362 Part VI: Quantitative Methods and Tools

Part VI.A.3

Raw data: .127 .125 .123 .123 .120 .124 .126 .122 .123 .125 .121 .123 .122 .125 .124 .122 .123 .123 .126 .121 .124 .121 .124 .122 .126 .125 .123 Value .120 .121 .122 .123 .124 .125 .126 .127

Figure 32.1

Tally | ||| |||| ||||||| |||| |||| ||| |

Example of tally or check sheet.

Tally or Check Sheet A tally or check sheet consists of a column of potential values usually shown from smallest to largest. As measurements are read, a tally mark is placed next to the appropriate value. Although no sophisticated analysis is provided, the tally sheet is very simple to use and understand. An illustration of such a sheet is shown in Figure 32.1. The data represent a sample of diameters from a drilling operation.

Automatic Gauging Data also may be collected by automatic gauging equipment. Potential advantages of this approach include improved precision as well as reduction of labor, time, error rates, and costs. When considering automated inspection, CQEs must pay attention to the possibility of high initial costs, including the possibility of part redesign to adapt the part to the constraints of the measurement system. If the measured values are fed directly into a database, care must be taken to make certain that the communication link is reliable and free of noise.

Data Coding Coding data can simplify recording and analysis. Sometimes it is useful to code data using an algebraic transformation. Suppose a set of data has mean m and standard deviation s (see section 5 of this chapter for more details on m and s ). A new set of data may be formed using the formula

EXAMPLE 32.1 A dimension has values that range from 1.031 to 1.039. For convenience it is useful to code these numbers using digits from 1 to 9 so that: 1 → 1.031 2 → 1.032 and so on.



m y = am + b

sy = as

For further information on the affect of algebraic transformations, see Hogg and Tanis (1997).

Part VI.A.4

y = ax + b. That is, each element of the new set is formed by multiplying an element of the original set by a, then adding b. The mean m y and standard deviation s y of the new set are:

4. DATA ACCURACY

Describe the characteristics or properties of data (e.g., source/resource issues, flexibility, versatility, etc.) and various types of data errors or poor quality such as low accuracy, inconsistency, interpretation of data values, and redundancy. Identify factors that can influence data accuracy, and apply techniques for error detection and correction. (Apply) Body of Knowledge VI.A.4

Common Causes of Errors The best data collection and analysis techniques can be defeated if the data have errors. Common causes of errors include: • Units of measure that are not defined (for example, feet or meters?). • Similarity of handwritten characters (for example, 2 or Z?). • Inadequate measurement system. • Rounding (generally should only be done at last stage of computation). • Batching input versus real-time input. • Inadequate use of validation techniques. • Multiple points of data entry. • Poor instructions or training. • Ambiguous terminology (for example, calendar or fiscal year? day ends at 3 PM or midnight?). For example, the NASA team working with the Mars rovers uses the term sol to designate a Martian day to avoid confusion with earth days. (Apparently yestersol refers to the previous Martian day.)



Part VI.A.4

Strategies to Avoid Data Errors Use strategies like these to minimize error: • Have a carefully constructed data collection plan. • Maintain a calibration schedule for data collection equipment. • Conduct gage repeatability and reproducibility (R&R) studies on data collection equipment. • Record appropriate auxiliary information regarding units, time of collection, conditions, measurement equipment used, name of the data recorder, and so on. • Use appropriate statistical tests to identify potential outliers. • If data are transmitted or stored digitally, use an appropriate redundant error-correction system. • Provide clear and complete instruction and training. If data are obtained through sampling, the sampling procedure must be appropriately designed. Some of the different techniques that can be used to establish a well-designed sampling strategy are discussed in the following paragraphs.

Random Sampling Simple random sampling is a procedure by which each item has an equal probability of being selected as part of the sample. One way to do this is to assign each item a number and create a set of numbered tags so that each tag number corresponds to exactly one item. The tags are thoroughly stirred in a container and one is drawn out. The number on the tag identifies which item is selected as part of the sample. If the population size is quite large, making up tags can be unreasonable. In this situation, random numbers generated by calculators or computer software such as Excel can be used to select the elements of the sample.

Stratified Sampling If the population of parts to be sampled is naturally divided into groups, it might be desirable to use stratified sampling. For example, suppose 300 of the parts came from Cleveland, 600 came from Chicago, and 100 came from Green Mountain. A stratified sample of size 50 could be formed by randomly selecting 15 items from the Cleveland batch, 30 from the Chicago batch, and five from Green Mountain. In other words, each group makes up a proportional part of the stratified sample.

Sample Homogeneity Sample homogeneity refers to the need to select a sample so that it represents just one population. Sample homogeneity is desirable regardless of the type of sampling. In the case of the stratified sampling procedure, the population consists of the original 1000 parts, and stratification is used to help ensure that the sample represents the various strata.



5. DESCRIPTIVE STATISTICS

Part VI.A.5

When selecting the data collection scheme for time-related data, the entire sample should be collected at the same time in the process so that it comes from the population being produced at 9:00 AM, not that produced at 9:15 AM, which may be a different population. In fact, the purpose of a control chart is to use sampling to determine whether the population produced at one time is different from the other populations sampled.

Describe, calculate, and interpret measures of central tendency and dispersion (central limits theorem) and construct and interpret frequency distributions including simple, categorical, grouped, ungrouped, and cumulative. (Evaluate) Body of Knowledge VI.A.5

The two principal types of statistical studies are descriptive and inferential. The purpose of descriptive statistics is to present data in a way that will facilitate understanding. Some important statistics that can be used to describe a set of data include: • A measure of the center of the population or a sample • A measure of the variability (measure of the spread of the data) for the population or a sample • A graphical display (displaying overall shape) of the data. Center, spread, and shape are key to understanding data and the process that generated them. The next few paragraphs discuss these attributes. (Complete definitions and discussion of population, sample, parameters, and statistics are provided in Chapter 34.)

Measures of Central Tendency Three ways to quantify the center of a data set are the mean (or average), median, and mode. The mean is the arithmetic average of a set of data or observations. The mean is also referred to as a “balancing point” for the set of observations. Suppose the data in a sample of size n are denoted by x1, x2, x3, . . . , xn. The sample mean, denoted x– (read “x-bar”), is given by n

x=

x1 + x2 + x3 + ... + xn = n

∑x i=1

n

i

.



Part VI.A.5

EXAMPLE 32.2 Emergency room waiting times are continually increasing. One factor that was identified as affecting wait time was turnaround time for basic blood analysis. Turnaround times (in minutes) for 10 such tests on one particular day are: 62

68

72

60

50

58

58

49

66

70

The average turnaround time is 6 2 + 68 + 72 + 60 + 50 + 58 + 58 + 49 + 66 + 70 10 = 61 . 3 min..

x=

If the data represent the entire population of interest, then the average is the population mean and commonly denoted by m. Suppose there are N observations in the population. The population mean is m=

x1 + x2 + x3 + ... + xN N N

=

∑x i=1

i

N

EXAMPLE 32.3 An accident investigator has been contracted by a large tire company to investigate accidents where the company’s tire may have been at fault. The investigator was contracted by this company for a total of six months before the company determined that his work was unacceptable. At the time his contract was terminated, the investigator had submitted a total of eight invoices for time spent at accident scenes. The amounts for each invoice are: $4390

$3285

$1582

$725

$3001

$2971

$463

$8923

Since these invoice amounts are the only amounts for this investigators’ work for the tire company, they represent the entire population of amounts. Therefore, the average invoice amount will be the population mean: x 1 + x 2 + x 3 + ... + x N N 4390 + 3285 + 1582 + 725 + 3001 + 2 971 + 463 + 8923 = = $ 3167 . 50 8

m=

The median is that value that divides ordered data into two equal parts—half of the data lies at or below that value and half of the data lies above that value. Suppose we have a sample of size n. If the sample contains an odd number of observations, the sample median is the central value. If there is an even number of



EXAMPLE 32.4 Consider the turnaround times for blood analysis given in Example 32.2, now written in increasing order: 49

50

58

58

60

62

66

68

70

Part VI.A.5

observations, the median is the average of the two central values. The location of the median for n observations is (n + 1)/2. The sample median is often denoted by M.

72

Since there are n = 10 observations in the data set, the location of the median is (10 + 1)/2 = 5.5. Therefore, the median is the average of the fifth and sixth observations from the smallest in the data set: 60 + 62 2 = 61 min.

M=

The sample mode is the observation that occurs most often in the sample. There can be more than one mode for a set of data. For example, the mode for the blood analysis turnaround times is 58 minutes, since it occurs more often than any other observation. As with the mean, the population median and population mode can be determined if the entire population is known. The mean and median are the most commonly used measures of the center. One final note on measures of the center: the median is known as a resistant measure of the center while the mean is not a resistant measure. The median is often used as the measure of the center for data that involves prices, salaries, or any data that may naturally contain extreme observations. The median is resistant to the influence of these extreme observations.

EXAMPLE 32.5 Housing prices in Glendale, Arizona, vary over a wide range. Suppose five houses on the market in May 2008 were listed at the following prices: $54,900

$75,000

$79,000

$101,500

$386,000

The average house price for this set of data is $139,280. Does the average appear to represent the sample of data itself? The price of $139,280 lies above all but one house price. The median house price for this set of data is $79,000. The average was pulled toward the extreme value of $386,000 while the median was not influenced by this particular value. Now suppose we discovered that the house priced $386,000 was reduced to $345,000. All other housing prices remained constant at the time of the data collection. With this reduction, the sample mean house price is now $131,080; the sample median house price remains at $79,000. Therefore, the median was resistant to the change in price while the mean was not resistant.



Part VI.A.5

Measures of Variability (Spread) Measures of variability describe the spread of the data around the center or central point of the distribution of the data. Three common measures of variation are the range, variance, and standard deviation. The sample range is the difference between the maximum value (max) and the minimum value (min) in the sample. The sample range is often denoted by R and given by: R = max – min For example, the sample range for the turnaround times given previously is R = 72 – 49 = 23 minutes. The sample variance is a measure of the variability based on the deviations of the actual observations from the mean. Suppose we have a sample of size n with observations x1, x2, . . . , xn. The sample variance is n

s2 =

∑(x i=1

i

− x)

2

.

n−1

EXAMPLE 32.6 Consider the blood analysis turnaround times (in minutes) given in Example 32.2: 49

50

58

58

60

62

66

68

70

72

The sample average was found to be x– = 61.3 minutes. The sample variance is n

s2 = =

∑( x i=1

i

− x)

2

n−1

( 49 − 61 . 3)2 + ( 50 − 61 . 3)2 + .... + (72 − 61 . 3)2 10 − 1

= 7 . 89 min2 .

The population variance, denoted by s 2, can be determined if the data from the entire population are given. Suppose the population consists of N observations and the population mean is given by m. The population variance is N

s2 =

∑(x i=1

i

− m)

N

2

.

Notice that the unit of measure of the variance is the square of the unit of measure of the original data and the mean. It is more convenient to have summary statistics (such as the measure of the center and measure of variability) in the same unit



n

s=

∑(x i=1

i

− x)

2

n−1

Part VI.A.5

of measure as the original data. The measure of variability that is in the same unit of measure as the original data and mean is the standard deviation. The standard deviation is simply the positive square root of the variance. The sample standard deviation is

and the population standard deviation is N

s =

∑(x i=1

i

− m)

N

2

.

For example, the sample standard deviation for the blood analysis turnaround times is s = 7 . 89 = 2 . 81 minutes.

Shape of the Data The shape of the sample or population refers to the form the data takes on when it is plotted in a graphical display. Graphical displays include but are not limited to dot plots, box-and-whisker plots, and stem-and-leaf plots (histograms are presented in Chapter 27). For example, a dot plot (also known as a dot diagram) for the blood analysis turnaround times is shown in Figure 32.2. The display reveals the spread of the data as well as possible outliers. This and other graphical displays will be discussed later in this chapter as well as in Chapter 34. There are numerous possible shapes that data can assume. Consider Figure 32.3. Figure 32.3a represents a histogram of the diameters from a drilling operation given in Section 3 of this chapter. The shape of this distribution is symmetric (bell-shaped) and there do not appear to be any potential outliers or unusual observations. Figure 32.3b displays lifetime data of a manufactured part. The lifetime data follows a skewed distribution, specifically a right-skewed distribution. Figure 32.3c displays data representing time to show symptoms in rats that have been subjected to a particular treatment. This is a left-skewed distribution. Finally, Figure 32.3d represents a bimodal distribution. This type of distribution has many applications, but sometimes this shape can indicate a mixed distribution of data (data may be coming from two different distributions).

51

54

57

60 x

63

66

69

72

Figure 32.2 Dot plot of blood analysis turnaround times.



Frequency

Frequency

Part VI.A.5

370 Part VI: Quantitative Methods and Tools

0.120 0.121 0.122 0.123 0.124 0.125 0.126 0.127 (a)

Diameter

2 (c)

4

6

8

10

2.5

5.0

7.5

10.0

12.5

15.0

17.5

x

8

12

x

Figure 32.3

0.0 (b)

12

16

(d)

20 x

24

28

32

Histograms of variously-shaped distributions.

Frequency and Cumulative Frequency Distributions A frequency distribution is a compact summary of data collected. The frequency distribution can be displayed in table form, in graphical form, or some functional form. An ungrouped frequency distribution in table form displays the individual observations and the number of times that each value appears in the data set. A frequency distribution of the diameters from the drilling operation is given in Table 32.1. The cumulative frequency distribution contains the observations themselves as well as the frequency of the occurrence of the current and preceding observations. The cumulative frequency distribution of the diameters from the drilling operation is given in the last column in Table 32.1.

Grouped Frequency Distributions Another way to present the diameter data from the previous example would be to group the measurements together as shown in Table 32.2.

Categorical Frequency Distributions If the data represent numbers of items in nonnumerical groups or categories, a categorical frequency distribution is used. An example of a categorical frequency



Table 32.1

Frequency and cumulative frequency distributions for the ungrouped diameter data. Frequency

Cumulative frequency

0.120

1

1

0.121

3

4

0.122

4

8

0.123

7

15

0.124

4

19

0.125

4

23

0.126

3

26

0.127

1

27

Table 32.2

Frequency and cumulative frequency distributions for grouped diameter data.

Group

Frequency

Cumulative frequency

0.120–0.121

4

4

0.122–0.123

11

15

0.124–0.125

8

23

0.126–0.127

4

27

Table 32.3 Defect type

Part VI.A.5

Measurement

Categorical frequency distribution of manufacturing defects. Frequency

Chip

3

Scratch

5

Ink smear

4

Fold mark

7

Tear

2

distribution is displayed in Table 32.3 for several important types of defects in a manufacturing process. Cumulative frequency distributions do not make sense when the groups are categories where order does not matter. Frequency distributions and cumulative frequency distributions provide a simple way to quickly examine the variability of data around the center. These



Part VI.A.6


distributions also aid in the calculation of statistics from the data such as the sample mean and sample standard deviation. In addition, data from the frequency and cumulative frequency distributions can easily be displayed graphically, such as in a histogram.

The Central Limit Theorem A frequent question in the minds of quality engineers is the validity of x– control charts when the population is not normally distributed (see Chapter 34 for discussion of the normal distribution). An important statistical principle in this situation is the central limit theorem. It states that The distribution of sample averages will tend toward a normal distribution as the sample size n approaches infinity. The central limit theorem guarantees at least approximate normality for the distribution of sample averages, even if the population from which the sample is drawn is not normally distributed. A more specific definition of the central limit theorem will be given in Chapter 34. For example, because the x– control chart involves plotting averages, the central limit theorem implies that normality is (approximately) guaranteed. The approximation improves as the sample size n increases. In some cases, the approximation will be applicable for sample sizes as small as 10. In other situations the required sample size for the approximation to be valid can be quite large (say, n > 100). Finally, if the underlying distribution of the data does not depart significantly from the normal distribution, sample sizes as small as n = 3 can be appropriate. For details on the normality assumption in statistical process control see Montgomery (2009b).

6. GRAPHICAL METHODS FOR DEPICTING RELATIONSHIPS

Construct, apply, and interpret diagrams and charts such as stem-and-leaf plots, box-and-whisker plots, etc. [Note: Run charts and scatter diagrams are covered in V.A]. (Analyze) Body of Knowledge VI.A.6

Stem and Leaf Plot A stem and leaf plot is constructed much like the tally column shown previously, except that the last digit of the data value is recorded instead of the tally mark. This kind of diagram often is used when the data are grouped. Consider the example shown in Figure 32.4.



Measurement

Tally

Stem and leaf

0.10–0.19 0.20–0.29 0.30–0.39 0.40–0.49 0.50–0.59

|||||| ||||||| |||||| ||||| |||

1 2 3 4 5

Figure 32.4

8 4 6 2 6

7 1 4 8 5

9 5 7 7 4

8 2 3 2

Ordered stem and leaf 9 1 4 6 4 8 9 4

1 2 3 4 5

1 1 3 2 4

7 2 4 2 5

8 4 6 4 6

8 4 7 7

9 9 4 5 6 8 9 8

Part VI.A.6

Data: .18 .24 .21 .17 .36 .34 .19 .25 .18 .22 .37 .24 .42 .33 .48 .56 .47 .55 .26 .38 .54 .19 .24 .42 .44 .11 .39 Units are 0.00

Stem and leaf diagrams.

62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 Figure 32.5

Box-and-whisker diagram.

The stem and leaf diagram conveys more information than the tally column or the associated histogram. Note that the ordered stem and leaf sorts the data and permits easy determination of the median.

Box Plots (Box-and-Whisker Diagrams) A sorted data set may be divided into four approximately equal subsets separated by three boundary points called quartiles. The quartiles are denoted Q1, Q2, and Q3. Q2 is defined as the median. Q1 is usually defined as the median of the values less than or equal to Q2. Q3 is median of the values greater than or equal to Q2. The inter-quartile range or IQR is Q3–Q1. The box plot (also called a box-andwhisker diagram), developed by Professor John Tukey of Princeton University, uses the high and low values of the data as well as the quartiles. This is illustrated in Figure 32.5. Some software packages, rather than extending the “whiskers” to the maximum and minimum values, terminate them at 1.5 × IQR above Q3 and 1.5 × IQR below Q1. Values beyond these whiskers are designated “potential outliers.” The data after sorting: 63, 65, 67, 69, 71, 71, 75, 76, 76, 76, 81, 85 Low value is 63, high value is 85, Q1 = 68, Q2 = 73, and Q3 = 76 Note that quartiles need not be values in the data set itself. The box plot of these data is shown in Figure 32.5. Figure 32.6 shows how the shape of the dot plot is reflected in the box plot. Box plots can be used to mine information from a database. In this hypothetical



Part VI.A.7


a) Approximately symmetric

Figure 32.6

b) Increased variability

c) Left-skewed

Box plots.

example, a stainless steel casting has a tight tolerance on the machined inside diameter. The quality team has heard a number of proposed fixes. Some people believe the problem is caused by a slightly out-of-round condition on a cross section of the casting. Others feel there is a taper and still others insist the problem is too much part-to-part variation. The question is, “Which type of variation is giving the most trouble?” The team decides to measure the ID at three angles (12 o’clock, 2 o’clock, and 4 o’clock) at three different locations along the bore (top, middle, and bottom) on five different pieces. The resultant data and box plots are shown in Figure 32.7. The box plots in Figure 32.7 show that the largest source of variation is part-topart. The Pareto principle says that the part-to-part variation should be attacked first. Furthermore, any improvements in out-of-round or taper may be masked by the large part-to-part variation. How would the box plot have looked if out-ofround or taper had been the principal source of variation?

7. GRAPHICAL METHODS FOR DEPICTING DISTRIBUTIONS

Construct, apply, and interpret diagrams such as normal probability plots, Weibull plots, etc. [Note: Histograms are covered in V.A]. (Analyze) Body of Knowledge VI.A.7

Graphical displays of data are important tools that can help determine important properties of the data. Using graphical displays, the overall shape, location of the center, and measure of variability can be approximately estimated. These displays can also be used to possibly determine the type of distribution that the data may follow. One graphical display that can be used for determining the type of distribution the data may follow is the probability plot.



T

Part #1 M B

Part #2 M B

.984 .982 .981 .982 .980 .982 .984 .983 .980

T

Part #3 M B

.998 .998 .997 .999 .998 .997 .996 .996 .996

T

Part #4 M B

.986 .987 .986 .985 .986 .986 .984 .985 .984

T

Part #5 M B

.975 .980 .976 .975 .976 .974 .978 .980 .974

Part VI.A.7

12 .998 .992 .996 2 .994 .996 .994 4 .996 .994 .995

T

.998 .997 .996 .995 .994 .993 .992 .991 .990 .989 .988 .987 .986 .985 .984 .983 .982 .981 .980 .979 .978 .977 .976 .975 .974

Figure 32.7

Multiple box plot example.

Probability Plotting The probability plot displays the actual data on the x-axis plotted against percentiles based on the hypothesized or assumed distribution of interest on the y-axis. For example, the normal probability plot displays the actual data against percentiles from a normal distribution. If the data fall—at least approximately—along a straight line, then the data is said to be approximately normally distributed. Probability plots can be constructed for many distributions including the normal, lognormal, Weibull, and exponential. Two of the more commonly used are the normal and Weibull probability plots.



Part VI.A.7

Normal Probability Plot The assumption of at least approximate normality is often necessary to satisfactorily apply many statistical tests. The normal probability plot can be used to determine if a given set of data come from a population that is normally distributed. In general, the data are plotted on a probability plot where the vertical axis has been scaled according to a normal distribution. It is unnecessary to create these plots manually. Most statistical software packages will generate these plots for any set of data. If the data fall at least approximately along a straight line, then the distribution of interest (in this case the normal distribution) is assumed to be a reasonable form for the data.

EXAMPLE 32.7 Sumithra and Bhattacharya (2008) present a study on the toasting of corn flakes. Appropriately toasted flakes possess the desired moisture content, texture, and color. In their study, the authors investigated the effect of three independent variables—moisture content, toasting temperature, and toasting time—on several responses of interest. One response was the force needed to puncture the toasted flake. The puncture force data (measured in Newton) for this experiment are: 5.34, 6.62, 2.90, 2.07, 5.87, 4.02, 3.45, 2.24, 3.80, 3.80, 2.27, 6.62, 3.95, 4.12, 2.95, 2.80, 2.81, 2.80, 2.90, 2.95 A normal probability plot of the puncture force data is given in Figure 32.8. The data do not appear to fall along a straight line. The normal distribution is probably not a reasonable model for puncture force. 99 95

Percent

90 80 70 60 50 40 30 20 10 5 0 0

Figure 32.8

1

2

3 4 Puncture force

5

6

7

Normal probability plot of puncture force for toasted corn flakes.



Weibull Probability Plot

Part VI.A.7

The Weibull probability plot is used to determine if a particular set of data follows a Weibull distribution. The Weibull distribution is often used in reliability problems (the Weibull distribution will be discussed in more detail in Chapter 34). Similarly to the normal probability plot, the Weibull probability plot uses Weibull probability paper, and the actual data are plotted against a percentile that is based on the Weibull distribution. The Weibull probability plot is more difficult to construct by hand than the normal probability plot and will not be outlined here. Many statistical packages have the capability to construct Weibull probability plots.

EXAMPLE 32.8 The following data represent the life of a particular part used in the semiconductor manufacturing industry. Fifteen parts are selected at random and their life (in hours) recorded when the parts are in use. The data are: 479.23, 43.17, 3219.41, 558.46, 56.00, 705.37, 12.02, 280.42, 3867.95, 6672.37, 8494.07, 1220.94, 66.92, 2078.13, 6431.02 It is important to determine the distribution that the data may follow. The Weibull distribution could be investigated. The Weibull probability plot for this set of data is shown in Figure 32.9. The data fall along a straight line. Therefore, the data appear to follow a Weibull distribution. The Weibull distribution appears to be valid for this set of data.

Percent

99 90 80 70 60 50 40 30 20 10 5 3 2 1 1

Figure 32.9

10

100 Life

1000

10,000

Weibull probability plot for life of a part.



Part VI.A.7

Comments on Probability Plotting Interpretation of probability plots can be subjective. One person may interpret the data as normally distributed, for example, while someone else examining the same plot could say that they are not normally distributed. The closer the data fall along a straight line, the more evidence there is that the distribution of interest is reasonable for that particular set of data. If the plot exhibits curvature or an “S” shape, then other distributions should possibly be investigated. Goodness-of-fit tests are often more reliable approaches to determining the appropriateness of a particular distribution.



Chapter 33 Part VI.B.1

B. Quantitative Concepts

T

his chapter lays the foundation for understanding how to draw statistical conclusions and how to apply probability terms and concepts.

1. TERMINOLOGY Define and apply quantitative terms, including population, parameter, sample, statistic, random sampling, expected value, etc. (Analyze) Body of Knowledge VI.B.1

A population is the entirety of all items or units being studied. A sample is a subset of items or measurements selected from the larger population. Since it is often impractical to obtain information on all the items or units in the population, we acquire information on a subset of them in order to draw conclusions about the rest. A parameter is a characteristic of a population. It is a quantity that describes characteristics of a population; for example, mean (m), standard deviation (s ), correlation coefficient (r), or fraction nonconforming (p). Often the value of the population parameter is unknown and must be estimated. A statistic is a characteristic of a sample and is an estimator of a population parameter. A representative sample is taken from the population and summary statistics calculated such as the average or mean (x–), standard deviation (s), correlation coefficient (r), or fraction nonconforming (pˆ ). In this case, x– is an estimator for m. Probability is a numerical measure representing the likelihood that a particular outcome will occur. The probability that a particular event occurs is a number between zero and one inclusive. For example, if a lot consisting of 100 parts has four nonconforming parts, we would say the probability that a randomly selected part will be nonconforming is 0.04 or four percent. The concepts can be summarized in the following example. 379



Part VI.B.2

EXAMPLE 33.1 The thickness of a printed circuit board (PCB) is an important characteristic. If the thickness does not meet specification, the circuit board is reworked or scrapped. The average thickness of the PCB is assumed to be 0.0630 inches (that is, m = 0.0630). Thickness of 25 randomly chosen PCBs is measured and the average thickness is found to be 0.06314 inches (that is, x– = 0.06314 inches). It is determined that the probability that the sample average diameter (for a sample size of 25) should have been 0.06314 inches or larger is 0.0105. In this particular problem: • The population is all printed circuit boards manufactured by this company with this process. • The sample is the 25 randomly chosen printed circuit boards. • The parameter is the population average or mean m and is assumed to be m = 0.0630 inches. • The statistic is the sample average or sample mean x– , calculated using the sample of 25 printed circuit boards, which was found to be x– = 0.06314 inches. • A probability associated with this sample is 0.0105.

2. DRAWING STATISTICAL CONCLUSIONS

Distinguish between numeric and analytical studies. Assess the validity of statistical conclusions by analyzing the assumptions used and the robustness of the technique used. (Evaluate) Body of Knowledge VI.B.2

Numeric Studies The purpose of inferential statistics is to infer (arrive at a conclusion by reasoning from evidence) properties of a population through analysis of a sample. This type of study is sometimes referred to as a numeric study. These studies are valid only if the sample is from a stable underlying population. For example, if a control chart is used on a stable process, the data from the chart can be used to conduct a capability study for the material produced while the chart was in use. This capability study infers information about the process population based on the sample used for the control chart, and would therefore be a numeric study.


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 33: B. Quantitative Concepts 381

Analytical Studies

Part VI.B.3

Sample data may also be used to study either stable or non-stable processes with the goal of process improvement, which may involve the use of knowledge, experience, creativity, and basic science. Such a study is not numeric because rather than infer properties of the population, the study seeks to determine the causes that impact the process. Inferential methods are inappropriate because the underlying population is often not stable and the goal is to change it rather than determine its characteristics. W. Edwards Deming called these analytical studies. A control chart, when used to take action on the process to maintain statistical control, is an example of a tool for analytical study. In Chapter 35 a number of statistical tests are described. Each has assumptions or conditions that must be met in order for the test to be valid. It is critical that a test’s assumptions or conditions are satisfied before applying the test. In some cases the discussion accompanying the statistical test may state that the test is robust to minor deviations from the assumptions or conditions. For example, if one of the conditions of a test is that the population be normal, the test may be robust to minor deviations in this condition. This means that even if the population is almost normal, the test could be applied, with caution about the precision of the conclusion. Needless to say, decisions in situations like this require judgment and experience.

3. PROBABILITY TERMS AND CONCEPTS

Describe and apply concepts such as independence, mutually exclusive, multiplication rules, complementary probability, joint occurrence of events, etc. (Apply) Body of Knowledge VI.B.3

Basic Definitions Before discussing probability and probability rules, it is important to define terms that describe the experiment under study. In this context, experiment refers to a random experiment where different outcomes could be obtained even if the experiment is repeated under identical conditions.

Sample Spaces and Events The sample space is the set of all possible outcomes of an experiment or a set of conditions (this is also referred to as the universal set in set theory). The sample space is usually denoted by the capital letter S. If the outcomes are finite or countably



Part VI.B.3


infinite, then the sample space is considered discrete. If the outcomes are values over an interval of real numbers, then the sample space is considered continuous. (Further discussion of continuous and discrete random variables and distributions is given in Chapter 34.) An event is a subset of the sample space and is often denoted by a capital letter such as A, B, C, and so on. If an outcome x is an element or outcome in A, for example, we write this as x ∈ A. To illustrate these concepts, consider an experiment where a single piston ring for an automobile motor is randomly selected from a lot and classified as conforming (C) or nonconforming (N). The sample space for this experiment is S = {C, N}. If we are only interested in the event where the piston ring is nonconforming (call this event E), this event would be E = {N}. As a second illustration, suppose the experiment was to determine how long it takes a worker to complete a task (in minutes). Let x represent the time to complete the task. The sample space consists of all positive real numbers. This can be written as S = {x|x > 0}. If the event or sample space has no outcomes in it, then we say it is the empty set, denoted Ø.

Set Operations It is often of interest to combine events to form other events in which we are interested. There are three basic set operations used to create new events of interest: • The union of two events A and B is that event consisting of all outcomes contained in A, in B, or in both. The union is denoted as A ∪ B (read “A or B”). • The intersection of two events A and B is that event consisting of all outcomes that are contained in both A and B. The intersection is denoted as A ∩ B (read “A and B”) • The complement of any event in a sample space is an event that contains all the outcomes in the sample space that are not in the event itself. The complement of an event A is denoted as A′ (read “A complement” or “not A”). Other notation used to represent the – complement of an event includes Ã, Ac, and sometimes A. We will use the prime notation, A′. It should be noted that if the intersection of any two events results in the empty set (that is, A ∩ B = Ø), then those two events are called mutually exclusive.

EXAMPLE 33.2 A simple illustration involves the rolling of a single, fair six-sided die. In this random experiment, the sample space is S = {1, 2, 3, 4, 5, 6}. Suppose A is the event where the outcome on a single roll is an even number; so A = {2, 4, 6}. Suppose B is the event where the outcome on a single roll is greater than 3; so B = {4, 5, 6}. In this situation: • A ∪ B = {2, 4, 5, 6} Continued



Continued

• A′ = {1, 3, 5}, B′ = {1, 2, 3} • A ∩ A′ = Ø (and B ∩ B′ = Ø); the intersection of an event and its complement is always the empty set • A ∪ A′ = S (and B ∪ B′ = S); the union of any event and its complement always equals the sample space

Part VI.B.3

• A ∩ B = {4, 6}

Probability As stated previously, probability is a numerical measure that represents the likelihood that a particular outcome will occur. The probability that a particular event occurs is a number between zero and one inclusive. The probability of an event, say E, is written as P(E). If the elements of E are mutually exclusive, then P(E) is equal to the sum of the probabilities of the outcomes that make up that event. To illustrate, suppose an event E contains elements a, b, c, d, and e; that is, E = {a, b, c, d, e}. Then the probability of event E would be P(E) = P(a) + P(b) + P(c) + P(d) + P(e). Suppose there are N possible mutually exclusive outcomes in an experiment, all equally likely to occur (such as in the rolling of a fair six-sided die). The probability of any one outcome would then be 1/N. Another way to look at probability is as a relative frequency. That is, the probability of an outcome would be the number of times that outcome occurs divided by the total number of possible outcomes.

Probability Rules Consider a sample space S and two events A and B from that sample space. Then 1. P(S) = 1 2. 0 ≤ P(A) ≤ 1 3. P(A′) = 1 − P(A) 4. If two events A and B are mutually exclusive, then P(A ∩ B) = 0. An important result of rule 4 is sometimes referred to as a special addition rule for mutually exclusive events, and is given as P(A ∪ B) = P(A) + P(B). In other words, when two events are mutually exclusive, the probability that an outcome in event A will occur, an outcome in event B will occur, or an outcome in both A and B will occur can be found by adding the individual probabilities of each event.



Part VI.B.3

EXAMPLE 33.3 Suppose the number of medication errors that occur for a patient at a particular hospital have the following probabilities: Number of medication errors

0

1

2

3

Probability

0.90

0.07

0.02

0.01

Let A be the event of at most one medication error occurring, that is, A = {0, 1}. Let B be the event where exactly two medication errors occur; that is, B = {2}. We note that these events are mutually exclusive. For this situation: • P(A) = P(0) + P(1) = 0.90 + 0.07 = 0.97 • P(B) = 0.02 • P(A′) = 1 – P(A) = 1 – 0.97 = 0.03 • P(A ∩ B) = 0 • P(A ∪ B) = P(A) + P(B) = 0.97 + 0.02 = 0.99

General Addition Rule The special addition rule given above applies only to experiments where the two events of interest have no outcomes in common (mutually exclusive). When the events are not mutually exclusive, a more general addition rule applies: P(A ∪ B) = P(A) + P(B) – P(A ∩ B)

EXAMPLE 33.4 Cellular phones are put through several inspections before being shipped to the customer. Two defect types are of significant importance: critical (C) and major (M) defects. Phones with either critical or major defects are completely reworked. Using recent inspection data, it was determined that two percent of the cell phones have critical defects only, five percent have major defects only, and one percent have both critical and major defects. The manufacturer wants to know what percent of all phones would require complete rework. In this situation, the information given is: • P(C) = 0.02 • P(M) = 0.05 • P(C and M) = P(C ∩ M) = 0.01 Complete rework is necessary if the phone has critical or major defects or both. This is the event C ∪ M. The percent of all phones needing rework is then given by P(C ∪ M). Using the addition rule, the percent of phones needing rework would be Continued



Continued

Based on this information, roughly six percent of the cell phones will need rework.

Table 33.1

Part VI.B.3

P(C ∪ M) = P(C) + P(M) – P(C ∩ M) = 0.02 + 0.05 – 0.01 = 0.06.

Contingency table of part color and part size. Red

Yellow

Green

Blue

Totals

Small

16

21

14

19

70

Medium

12

11

19

15

57

Large

18

12

21

14

65

Totals

46

44

54

48

192

Contingency Tables Suppose each part in a lot is one of four colors—red (R), yellow (Y), green (G), or blue (B)—and one of three sizes—small (S), medium (M), or large (L). These attributes can be displayed in a contingency table like the one in Table 33.1. (Contingency tables are also used to determine statistical independence of characteristics. This application is discussed in Chapter 35.) It is often useful to include the row and column totals for calculating quantities of interest, such as probabilities. The total number of parts (N = 192) is written in the bottom right-hand corner of the table. The row and column totals provide a great deal of information about the categories of interest. For example, the column total for red is 46. This indicates that the total number of red parts (regardless of size) in the lot is 46. In addition, the row total for medium parts is 57, which means that the total number of medium parts (regardless of color) is 57. The entries in each cell of the table itself (not including the row and column totals) represent the number of parts that have both characteristics. For example, 16 parts are both small and red.

EXAMPLE 33.5 We want to determine several probabilities using the contingency table given as Table 33.1. Assume that one of the parts is selected at random. The probability that the part is small would be P(S) =

70 = 0 . 365 . 192 Continued



Part VI.B.3

Continued

The probability that the part is red would be P(R ) =

46 = 0 . 240 . 192

The probability that the part is small and red would be P(S ∩ R ) =

16 = 0 . 083 . 192

The probability that the part is small or red would be P(S ∪ R ) = P(S) + P(R ) − P(S ∩ R ) = 0 . 365 + 0 . 240 − 0 . 083 = 0.. 522 . Using the same formulas, the probability that a randomly selected part is yellow would be P(Y) = 0.229. The probability that the part is red or yellow would be P(R ∪ Y ) = P(R ) + P( Y ) − P(R ∩ Y ) = 0 . 240 + 0 . 229 − 0 = 0 . 469 Notice that the events “red” and “yellow” are mutually exclusive (a part cannot be both red and yellow). We could have used the special addition rule to find this probability: P (R ∪ Y ) = P (R ) + P ( Y ) = 0 . 240 + 0 . 229 = 0 . 469

Conditional Probability We will begin the discussion of conditional probability with an example.

EXAMPLE 33.6 Continuing with the previous example, suppose the selected part is known to be green. With this knowledge, what is the probability that the part is large?

Solution: It is a given that the part is one of the 54 green parts. Now, the number of the 54 green parts that are large is 21. Therefore, the probability that a part will be large, given that it is green would be 21/54 = 0.389.



Formal Definition. Suppose there are two events A and B. The probability that event A occurs given that event B has already occurred is P(A|B) =

Part VI.B.3

Example 33.6 involves conditional probability. It is referred to as conditional probability since it is conditioned on the fact that the part is green. In the example, the “probability that the part is large given that it is green” is denoted P(L|G). It is useful to remember that the category to the right of the | sign represents the given condition.

P(A ∩ B) . P(B)

EXAMPLE 33.7 Using the information given in Table 33.1, find: a. The probability that a part is small given that it is blue b. The probability that a small part is blue c. The probability that a green part is red

Solution: a. P(S | B) =

P(S ∩ B) 19 = = 0 . 396 P(B) 48

b. The given condition is that the part is small. Given that the part is small, the probability that it is blue is P(B | S) =

P(B ∩ S) 19 = = 0 . 271 . P(S) 70

Note that P(B ∩ S) = P(S ∩ B). c. P(R | G) =

P(R ∩ G) 0 = =0 P(G) 54

Note that red and green are mutually exclusive, so P(R ∩ G) = 0.

General Multiplication Rule If the practitioner has any two of the three probabilities needed to calculate the conditional probability given earlier, the third unknown probability can be found. The conditional probability can be rewritten as P(A ∩ B) = P(A|B)P(B). This is sometimes referred to as the general multiplication rule. Verifying that this formula is valid will aid in understanding this concept.



Part VI.B.3

EXAMPLE 33.8 Using the contingency table given as Table 33.1, it is known that the probability a part is red and medium is P(R ∩ M) =

12 = 0 . 0625 . 192

Using the general multiplication rule, we would get the same result: P(R ∩ M) = P(R | M)P(M) =

12 ⎛ 57 ⎞ 12 = 0 . 066 25 ⎜ ⎟= 57 ⎝ 192 ⎠ 192

Independence and the Probability of Independent Events Events are said to be independent if the occurrence of one event does not depend on the occurrence or lack of occurrence of another (or preceding) event. The probability of two independent events occurring can be found by multiplying the individual probabilities of each event. If two events A and B are independent of one another, then the probability of both event A and event B occurring is P(A ∩ B) = P(A) × P(B) For more than two independent events, the independence rule can be extended as P(A ∩ B ∩ C ∩ ...) = P(A) × P(B) × P(C) × ...

EXAMPLE 33.9 Assume that the probability that a blood specimen contains high levels of lead contamination is 0.05. Levels of contamination from one person to the next (thus, one sample to the next) are assumed to be independent. If two such samples are analyzed, then the probability that both will contain high levels of contamination is P(both contaminated) = P(1st contaminated ∩ 2nd contaminated) = P(1st contaminated) × P(2nd contaminated) = (0.05)(0.05) = 0.0025

Conditional Probability and Independence Recall that conditional probability is given by P(A|B) =

P(A ∩ B) . P(B)



P(A|B) =

P(A ∩ B) P(A)P(B) = = P(A). P(B) P(B)

In other words, knowing that event B has occurred does not affect the probability that event A will occur. In situations where objects or items are selected at random, one after the other, the items are said to be independent if the first item chosen is placed back into the group before the second item is chosen (with replacement).

Part VI.B.3

If two events A and B are known to be independent, then P(A ∩B) = P(A)P(B). Therefore, if two events are independent, the probability that event A occurs given that event B has already occurred would be

EXAMPLE 33.10 A box holds 129 parts, of which six are defective. A part is randomly drawn from the box and placed in a fixture. A second part is then drawn from the box. This is referred to as drawing without replacement. What is the probability that the second part is defective (note that there is no condition on the first part chosen)? Let Di represent the event where the ith part chosen is defective and let G i represent the event where the ith part is good.

Solution: We are looking for P(D2). There are two mutually exclusive events that can result in a defective part on the second draw: good on first draw and defective on second or else defective on first and defective on second. Symbolically these two events are (G1 ∩ D2) or else (D1 ∩ D2). The first step is to find the probability for each of these events. By the general multiplication rule: P(G1 ∩ D2 ) = P(G1 )P(D2 | G1 ) =

123 ⎛ 6 ⎞ ⎜ ⎟ = 0.045 129 ⎝ 128 ⎠

P(D1 ∩ D2 ) = P(D1 )P(D2 | D1 ) =

6 ⎛ 5 ⎞ ⎜ ⎟ = 0 . 002 129 ⎝ 128 ⎠

and

Since the two events (G1 ∩ D2) and (D1 ∩ D2) are mutually exclusive, we can use the special addition rule in order to find the probability that the second part is defective: P(D2) = 0.045 + 0.002 = 0.047

EXAMPLE 33.11 Consider the information given in Example 33.10. When drawing two parts at random without replacement, what is the probability that one will be good (call this G without a subscript) and one defective (call this D without a subscript)? Continued



Part VI.B.3

Continued

Solution: Drawing one good and one defective can occur in two mutually exclusive ways: P(G ∩ D) = P(G1 ∩ D2 ) + P(G2 ∩ D1 ) From the previous example we know that P(G1 ∩ D2) = 0.045. Use the general multiplication rule to find P(G 2 ∩ D1): P(G2 ∩ D1 ) = P(D1 )P(G2 | D1 ) =

6 ⎛ 123 ⎞ ⎜ ⎟ = 0.045 129 ⎝ 128 ⎠

Therefore, the probability that one randomly selected part will be good and one will be defective is P(G ∩ D) = 0 . 045 + 0 . 045 = 0 . 090 .

Summary of Key Probability Rules For events A and B: Special addition rule: P(A ∪ B) = P(A) + P(B) (Use only if A and B are mutually exclusive) General addition rule: P(A ∪ B) = P(A) + P(B) – P(A ∩ B) (Always true) Special multiplication rule: P(A ∩ B) = P(A)P(B) (Use only if A and B are independent) General multiplication rule: P(A ∩ B) = P(A)P(B|A) (Always true) Conditional probability: P(B|A) = P(A ∩ B)P(A) Mutually exclusive (or disjoint): 1. A and B are mutually exclusive if they cannot occur simultaneously. 2. If A and B are mutually exclusive, then P(A ∩ B) = 0. 3. If A and B are mutually exclusive, then P(A ∪ B) = P(A) + P(B). Independence: 1. A and B are independent events if the occurrence of one does not change the probability that that other occurs. 2. If A and B are independent events, then P(B|A) = P(B) (or P(A|B) = P(A)). 3. If A and B are independent events, then P(A ∩ B) = P(A)P(B).



Sampling Distribution of the Sample Mean

Part VI.B.3

Another important statistical principle refers to the distribution of sample means. The sample mean is a statistic calculated from a sample of data. Since the sample mean can take on different values for different samples taken from the same population, the sample mean is a random variable. As a random variable, the sample mean has its own distribution. The distribution of the sample mean is referred to as the sampling distribution of the sample mean. An important statistical principle states: If samples of size n are randomly drawn from a population with mean m and standard deviation s, then the distribution of sample means has the following properties: • Its mean, denoted m x–, is equal to the population mean: m x– = m • Its variance, denoted s x–2, is equal to the population variance divided by the sample size n: s x2 =

s2 n

• Its standard deviation is equal to the positive square root of the variance: sx =

s n

Also, the standard deviation of the sampling distribution of x– is referred to as the standard error.

EXAMPLE 33.12 A process with mean m = 1.27 and standard deviation s = 0.17 is monitored with a control chart using samples of size five. Compute the centerline and approximate the upper and lower control limits for the x– control chart. Assume that the underlying distribution is normally distributed. (Normal distributions will be discussed in more detail in Chapter 34).

Solution The points on the control chart are means of samples taken from the process’s population. Therefore, the centerline for the control chart is : mx– = m = 1.27. Sometimes, you will see the control limit formulas use the notation : mx– instead of : mx– . Conventional control limits are : mx– ± 3s x– . The standard error is sx =

0 . 17 s = = 0 . 076 . n 5 Continued



Part VI.B.3

Continued

So, the control limits are m x ± 3s x = 1 . 27 ± 3( 0 . 076 ) = 1 . 27 ± 0 . 228 or (1.042, 1.498).

Expected Value The expected value of any experiment is that value we would expect to be the average response in the long run if the experiment could be run indefinitely. To illustrate, when flipping a coin there is a 50/50 chance of getting heads or tails. In other words, we would expect heads to appear 50 percent of the time and we would expect tails to appear 50 percent of the time. Thus, in tossing an honest coin, the expected value for the fraction of heads obtained would be 1/2. This rarely happens in real life. There are nearly always a few more or a few less heads than tails. But if we tossed the coin a thousand times, the number of heads divided by the number of tosses would be very close to 1/2, and even closer if we tossed it 100,000 times. In theory, the expected value of any single observation xi taken from a random sample is equal to the mean of the population m from which the observation has come. The notation is given as E(xi) = m. In essence, this states that an unknown value xi from some population of interest would be expected to be equal to the true population mean.

EXAMPLE 33.13 A part is selected at random from a population. The true diameter of this population of parts is believed to be 12 mm (that is, m = 12). Therefore, without actually measuring the part selected at random, we would expect the diameter to be 12 mm (that is, E(xi) = 12). Now, the expected value of the sample mean is also m , the mean of the population. The notation for expected value of the sample mean is E(x–) such that E(x–) = m for a sample of size n.

Example 33.14 summarizes the use of expected value and standard error of the mean.



EXAMPLE 33.14

Part VI.B.3

A sample of 100 parts is selected at random from a population. The true diameter of the population of parts is believed to be 12 mm (m = 12) with a standard deviation of 0.05 mm (that is, s = 0.05). Therefore, we would in theory expect the sample of 100 parts to have an average diameter of 12 mm or E(x–) = m = 12. The standard error of the sample mean is found as sx =

0 . 05 s = = 0 . 005 . n 100

The value 0.005 represents the variability associated with the 100 parts chosen at random.

Expected value will be further discussed in Chapter 34 on probability distributions.



Part VI.C.1

Chapter 34 C. Probability Distributions

T

his chapter focuses on the two kinds of distributions: continuous distributions and discrete distributions. Both distributions will be discussed in this chapter.

• Continuous distributions are used when the parameter being measured can be expressed on a continuous scale. Examples include the diameter of piston rings, tensile strength, output voltage, and so on. • Discrete distributions are used when the parameter being measured takes on only certain values, such as integers 0, 1, 2, . . . . Examples include the number of defects or the number of nonconformities.

1. CONTINUOUS DISTRIBUTIONS

Define and distinguish between these distributions: normal, uniform, bivariate normal, exponential, lognormal, Weibull, chi square, Student’s t, F, etc. (Analyze) Body of Knowledge VI.C.1

Before commencing discussion of the continuous distributions, two important concepts must be introduced: the probability density function (pdf) and the cumulative density function (cdf). These are also referred to as probability distribution functions and cumulative distribution functions.

Probability Density Function Probability density functions are mathematical expressions that describe the probability distribution of a continuous random variable. The pdf is denoted by f(x). In most cases, the probabilities associated with some random variable can be described by a probability density function. Figure 34.1 represents a probability

394


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 34: C. Probability Distributions 395

f(x) f(x)

Part VI.C.1

P(a < X < b)

a

b x

Figure 34.1 A probability density function for a random variable X.

density function for a random variable X. The x-axis represents all possible values of the random variable; the y-axis represents the probability density function f(x). Suppose we wish to find the probability that our random variable X would lie between two real numbers a and b (that is, P(a < X < b)). Graphically, this probability is the shaded area under the curve f(x) and between the X values of a and b (see Figure 34.1). Definition of a Probability Density Function. For any continuous random variable X, the probability density function f(x) is a function with the following properties: a. f(x) ≥ 0 ∞

b.

∫

f ( x)dx = 1

−∞ b

c. P ( a ≤ X ≤ b ) = ∫ f ( x)dx ; this is the area under the curve f(x) and a between the values a and b The first property (property a) guarantees that all probability values are nonnegative. The second property (property b) can be compared to the concept of sample space given in Chapter 32. That is, the total area under the curve must equal one (or 100 percent) and can be verified by integrating f(x) over all real numbers. Property c simply describes how the probability that X will lie between two real numbers a and b can be determined by integrating f(x) over the range [a, b]. Although it may seem complicated, calculus is not necessary to find most of the probabilities that we need in quality engineering. Tables with probabilities for specific distributions are available as well as software that routinely calculates these probabilities. It



Part VI.C.1

should be noted that for continuous distributions, the probability that the random variable equals some specific value is always zero, that is P(X = a) = 0. As a result P(a ≤ X ≤ b) = P(a < X ≤ b) = P(a ≤ X < b) = P(a < X < b).

Cumulative Distribution Functions A cumulative distribution function (cdf) is denoted by F(x) and describes the cumulative probability for a random variable X: x

F( x) = P(X ≤ x) =

∫

f ( v)dv

−∞

The cdf can be used to find probabilities of interest for the random variable X. Suppose a and b are any real numbers where a < b. Then: • P(X < a) = F(a) • P(a < X < b) = P(X < b) – P(X < a) = F(b) – F(a) • P(X > a) = 1 – P(X < a) = 1 – F(a)

Normal Distribution An important family of continuous distributions is the normal distribution. The normal distribution is a symmetric, bell-shaped distribution. The parameters of the normal distribution are the population mean m and the population variance s 2. The normal distribution is depicted in Figure 34.2. The centerline represents the mean of the distribution. Graphically, the standard deviation is the distance between the centerline and the point at which the down slope of the curve meets the up slope of the curve (that is, point of inflection).

Standard deviation

Mean

Figure 34.2 Probability density function (pdf) for the normal distribution.



Z=

X−m . s

Part VI.C.1

A property of normal distributions is that the area under the curve represents probability (or percentage or proportion). The probability can be determined using the standard normal curve table in Appendix F. In a normal distribution, a Z-score for a random variable X is defined as the number of standard deviations between X and the mean of the distribution. Specifically,

To illustrate, suppose X follows a normal distribution with mean m = 20 and standard deviation s = 4. The Z-score for an X value of 14 would be Z=

X − m 14 − 20 = = −1.5 s 4

That is, the value of 14 is 1.5 standard deviations below the population mean of 20. By transforming the original random variable into a Z-score, we are able to find probabilities without having to use calculus.

Standard Normal Distribution If a random variable is normally distributed with mean m = 0 and variance s 2 = 1, it is called a standard normal random variable and often denoted as Z. Probabilities for the standard normal distribution are given in Appendix F. The values in this table are the cumulative probabilities P(Z ≤ z), where the capital letter Z represents a random variable and the lower case letter z is a real number. The cumulative probabilities in Appendix F can be used to find any probability of interest involving a random variable that is normally distributed. Some examples illustrating the use of Appendix F follow.

EXAMPLE 34.1 Let Z be a random variable that follows a standard normal distribution. Find the probability that Z will be less than 2.5.

Solution: The probability of interest is P(Z < 2.5). This probability is shown graphically in Figure 34.3. It shows that the probability we are interested in is the area to the left of 2.5. In Appendix F, the values of Z are written down the left-hand column and across the top of the table. The entries in the body of the table are the cumulative probabilities, P(Z ≤ z). In this example our z value is 2.5. In the table, read down the left-hand column to find the value 2.5, then across until you reach the column heading of 0 (since the value in the second decimal place of our z value is 0). The entry in the body of the table for the row of 2.5 and the column of 0 is 0.9938. Therefore, the probability that the random variable Z is less than 2.5 is 0.9938. Continued



Continued

Part VI.C.1

P(Z < 2.5)

0

Figure 34.3

2.5

z

Probability density function for a standard normal distribution.

EXAMPLE 34.2 Find the probability that the random variable Z is greater than –2.5.

Solution: The probability of interest is P(Z > –2.5). From the table in Appendix F, the probability given in the body of the table for –2.5 is 0.0062. But the probabilities in this table are the probabilities that Z is at most that value. That is, P(Z < –2.5) = 0.0062, and we want P(Z > –2.5). To find this probability, use the result that the total area under the curve (total probability) must equal 1. Therefore, if P(Z < –2.5) = 0.0062, then P(Z > –2.5) = 0.9938. More generally, we can find this probability as follows: P(Z > –2.5) = 1 – P(Z < –2.5) = 1 – 0.0062 = 0.9938

EXAMPLE 34.3 Find the probability that Z lies between 1.42 and 2.33.

Solution: The probability of interest is P(1.42 < Z < 2.33). The probability is displayed graphically in Figure 34.4. The area (thus the probability) of interest lies under the curve and between 1.42 and 2.33, as illustrated in Figure 34.4. The probability of interest can be found using the cumulative probabilities from Appendix F and subtraction: P(1.42 < Z < 2.33) = P(Z < 2.33) – P(Z < 1.42) = 0.9901 – 0.9222 = 0.0679 Continued



Continued

P(Z < 1.42)

P(1.42 < Z < 2.33)

0

Figure 34.4

1.42

2.33

Part VI.C.1

P(Z < 2.33)

z

Probability density function for a standard normal distribution.

We can now find the probability for any normal random variable by using a simple transformation. Definition. If X is a random variable that follows a normal distribution with mean m and variance s 2, then the random variable Z, where Z=

X−m , s

is also normally distributed with a mean m = 0 and variance s 2 = 1. That is, Z is a random variable that follows a standard normal distribution.

EXAMPLE 34.4 A product-fill operation produces net weights that are normally distributed with mean m = 8.06 ounces and standard deviation s = 0.37 ounces. Estimate the percent of the containers that have a net weight less than 7.08 ounces.

Solution: Let X represent the weight of the containers. The probability of interest is P(X < 7.08). Transform X into the random variable Z using the relationship X−m , s then from Appendix F find the appropriate probability. Z=

⎡ X − m 7 . 08 − 8 . 06 ⎤ P ( X < 7 . 08 ) = P ⎢ < ⎥⎦ = P ( Z < − 2 . 6 5) = 0 . 0040 0 . 37 ⎣ s This indicates that approximately 0.40 percent of the containers have a net weight less than 7.08 ounces. This can also be stated as the probability that a randomly selected container will have a net weight less than 7.08 is approximately 0.0040.



Part VI.C.1

f(x)

0.0

1.5

3.0

4.5

6.0

7.5

9.0

x

Figure 34.5

Probability density function for an exponentially distributed random variable.

Exponential Distribution The exponential distribution is a continuous probability distribution often used to model problems in reliability. In particular, the exponential distribution models the time or distance between successive events (such as failures) when the events follow a Poisson distribution. The Poisson distribution is often a reasonable model of defects in material or the number of failures in systems. (The Poisson distribution for discrete data will be discussed in the next section.) When we wish to determine the average time between failures, we calculate the inverse of the average number of failures or defects. For example, if there are an average of 0.69 failures per hour, then the mean time between failure (MTBF) is 1/0.69 = 1.45 hours. Figure 34.5 displays an exponential distribution with a mean of 1.45. Definition. Suppose X represents the time or distance between successive events of a Poisson process with mean l (where l > 0). The random variable X is said to be an exponential random variable with parameter l. The pdf for X is f ( x) = le − lx, for x ≥ 0 . Definition. The cumulative density function for an exponentially distributed random variable X is given by F(x) = P(X ≤ x) = 1 – e–lx, for x ≥ 0.

EXAMPLE 34.5 The time between calls to a customer service center is an exponentially distributed random variable with a mean time between calls of two minutes. What is the probability that the next phone call will be received in the next one minute? Continued



Continued

Let X represent the time between phone calls received at the customer service center. X follows an exponential distribution with parameter l = 0.5 (recall that the mean of an exponential distribution is 1/l and the mean in this case is two minutes; thus l = 1/2 = 0.5). The probability of interest is P(X < 1) and can be found using the cdf for the exponential:

Part VI.C.1

Solution:

P(X < 1) = F(1) = 1 – e – 0.5(1) = 1 – 0.6065 = 0.3935 Therefore, the probability that the next phone call will be received within the next minute is 0.3935.

Expected Value and Variance Suppose the random variable X follows an exponential distribution with parameter l. The mean (expected value) of X is m = E(X ) =

1 . l

s 2 = V(X ) =

1 . l2

The variance of X is

Weibull Distribution The Weibull distribution is a commonly used distribution in areas such as reliability. The Weibull is extremely flexible in modeling failure distributions that can take on many different shapes. Let X represent a random variable that follows a Weibull distribution. The pdf for the Weibull distribution is: ⎛ b ⎞ ⎛ x −g ⎞ f ( x) = ⎜ ⎟ ⎜ ⎝ q ⎠ ⎝ q ⎟⎠

b −1

e

⎛ x −g ⎞ −⎜ ⎝ q ⎟⎠

b

, forr x > 0

where b is the shape parameter (b > 0) q is the scale parameter (q > 0) g is a threshold parameter (g > 0) This pdf is referred to as a three-parameter Weibull distribution. The threshold parameter allows the user to model a distribution that can not practically begin at zero. It simply shifts the beginning point away from zero. The pdf for the Weibull distribution with b = 1.2 and q = 20 is displayed in Figure 34.6.



Part VI.C.1

f(x)

x

Figure 34.6

Probability density function for the Weibull distribution.

The cdf for a random variable that follows a three-parameter Weibull distribution with parameters b, q, and g is P(X ≤ x) = F( x) = 1 − e

⎛ x −g ⎞ −⎜ ⎝ q ⎠⎟

b

The cdf can be used to easily find probabilities associated with the Weibull distribution. Expected Value and Variance. The expected value for the Weibull distribution is ⎞ ⎛1 E(X ) = m = g + q Γ ⎜ + 1⎟ ⎠ ⎝b where ⎞ ⎛1 Γ ⎜ + 1⎟ ⎠ ⎝b is a gamma function (see Montgomery and Runger [2006] or Devore [2007] for discussion of the gamma function). The variance for the Weibull distribution is 2 ⎡ ⎛2 ⎞ ⎤ ⎞ ⎛2 Var(X ) = s 2 = q 2 ⎢ Γ ⎜ + 1⎟ − Γ ⎜ + 1⎟ ⎥ ⎠ ⎥ ⎠ ⎝b ⎢⎣ ⎝ b ⎦

The beauty of the Weibull function is that it takes on many shapes depending on the value of b. For example, when b = 1, the function is exponential and when b = 3.5 the function is approximately the normal distribution. If the threshold



Continuous Uniform Distribution

Part VI.C.1

parameter is set equal to zero (that is, g = 0), then the three-parameter Weibull reduces to the two-parameter Weibull distribution—another commonly used distribution in reliability. The Weibull function is sometimes used for reliability data when the underlying distribution is unknown. This is discussed in more detail in Chapter 20.

The continuous uniform distribution is one that has a flat probability distribution between two points a and b. That is, if each value of the random variable has the same probability of occurring, the distribution is called the uniform distribution. The plot of a uniform distribution has a horizontal line as its upper boundary. An example is given in Figure 34.7. The pdf for the continuous uniform distribution on the interval [a, b] is ⎧ 1 , a≤x
x
The cdf can easily be used to find probabilities associated with the uniform distribution using the relationships shown previously in this section.

0.0550

0.0525

0.0500

0.0475

0.0450 20

25

30 x

35

40

Figure 34.7 Continuous uniform probability distribution.



Part VI.C.1

EXAMPLE 34.6 The thickness of a manufactured airplane part is uniformly distributed between 2.2 and 2.8 millimeters. We would like to find the probability that the thickness is less than 2.6 millimeters.

Solution: Let X represent the thickness of the airplane part. We want to find P(X < 2.6). Since we are looking for X less than some value, we have P(X < 2.6) = F(2.6). Since the number that we are interested in (2.6) lies between the endpoints 2.2 and 2.8, we will use F(X) = (x – a)/(b – a) from the cdf. The probability that the thickness is less than 2.6 millimeters is P(X < 2.6) = F(2.6) = (2.6 – 2.2)/(2.8 – 2.2) = 0.667.

Expected Value and Variance. The expected value (mean) and variance for a continuous uniform distribution over the interval [a, b] are E(X ) = m =

a+b 2

V(X ) = s 2 =

( b − a )2 12

Bivariate Normal Distribution If there are two variables of interest (such as length and width), each of which is normally distributed, the resulting distribution is called bivariate normal. The bivariate normal distribution can be used to describe the distribution of two normally distributed random variables, say X and Y, that are not necessarily independent. There are five parameters that describe the bivariate normal distribution: mX, mY, sX2, sY2, and r where: • mX is the mean of the random variable X • sX2 is the variance of the random variable X • mY is the mean of the random variable Y • sY2 is the variance of the random variable Y • r is the correlation between X and Y Computer software is employed for handling problems involving these distributions. The bivariate normal distribution can be illustrated through the following example.



EXAMPLE 34.7

Part VI.C.1

The inside and outside diameters of a particular type of tubing are important characteristics to be measured. Let X represent the inside diameter of the tubing and let Y represent the outside diameter of the tubing. The inside and outside diameters are assumed to be normally distributed but not independent. For this problem, mX = 26, mY = 39, sX2 = 0.16, sY2 = 0.09, and the correlation between the inside and outside diameters is assumed to be r = 0.96. It is important that the diameters both meet the specifications of several customers. Suppose the specifications for one particular customer for X are 25.2 to 26.4 and the specifications of Y are 38.5 to 40.9. The probability that both dimensions are within specifications at the same time is P(25.2 < X < 26.4, 38.5 < Y < 40.9) = 0.7922. This value was calculated using computer software.

Lognormal Distribution If a variable X follows a normal distribution, then the variable Y = eX follows a lognormal distribution. This distribution has applications in modeling life spans for products, response time, time-to-failure data, as well as certain economic variables. Some important properties of the lognormal distribution are: • It assumes only positive values • It is a right-skewed distribution • It is the distribution of the random variable whose logarithm follows the normal distribution Suppose X follows a normal distribution with mean mX and variance sX2, and Y = eX. We then say that Y follows a lognormal distribution with the following mean and variance: 2

m Y = E(Y ) = e mX +(1/2 )s X 2

2

V(Y ) = s Y2 = e 2 mX +s X (es X − 1) When the data follow a lognormal distribution, a transformation of data can be done to make the data follow a normal distribution so we can then find probabilities, construct confidence intervals, and conduct tests of hypotheses (all of which depend on the assumption that the logged data follow a normal distribution).

Sampling Distributions A sampling distribution is the probability distribution of a sample statistic. The following sampling distributions are used in the inferential statistics chapters to follow. Each of the following sampling distributions can be defined in



f (x)

Part VI.C.1

k=2

k=6 k = 10

0

10

20 x

Figure 34.8

Several chi-square distributions.

terms of normally distributed random variables. Their theoretical bases are introduced here. Chi-Square (b 2 ) Distribution. Suppose Z1, Z2, Z3, . . . , Zk are independent standard normal random variables. Then the random variable c 2 = Z12 + Z22 + Z32 + ... + Zk2 follows a chi-square distribution with k degrees of freedom. The probability density function for the chi-square distribution is 1

f ( x) = 2

k/2

⎛ k⎞ Γ⎜ ⎟ ⎝ 2⎠

x( k/2 )−1 e − x/2 , for x > 0 .

The expected value and variance for the chi-square distribution are m = k and s 2 = 2k, respectively. Several chi-square distributions are displayed in Figure 34.8. Student’s t Distribution. Let Z be a standard normal random variable and W be a c 2 random variable with k degrees of freedom where Z and W are statistically independent. Then the random variable T defined as T=

Z W k

has a probability density function given by



k = 10

Part VI.C.1

k=8

k=2

–6

Figure 34.9

–4

–2

0 x

2

4

6

Probability density functions for three t distributions.

⎛ k + 1⎞ Γ⎜ ⎝ 2 ⎟⎠ 1 × , for − ∞ < t < ∞ , f (t) = ( k + 1)/2 ⎛ k ⎞ ⎡ t2 ⎤ p kΓ ⎜ ⎟ ⎝ 2 ⎠ ⎢ k + 1⎥ ⎣ ⎦ which follows a t distribution with k degrees of freedom. For k > 1, the mean and variance for the t distribution are m = 0 and s 2 > 1. For k = 1, the t distribution is a Cauchy distribution, which has no mean or variance. Figure 34.9 displays t distributions for various degrees of freedom. The t distribution is very similar to the standard normal distribution since both are symmetric, bell-shaped, and have m = 0. However, the tails of the t distribution are heavier than the standard normal; in other words, there is more probability in the tails (extreme values) of the t distribution than in the standard normal distribution. Notice that as the degrees of freedom go to infinity the form of the Student’s t distribution becomes the standard normal distribution. F Distribution. Suppose Y and W are independent chi-square random variables with k1 and k2 degrees of freedom, respectively. Then the random variable F=

Y / k1 W / k2

is said to follow an F distribution with k1 and k2 degrees of freedom.

Summary of Continuous Distributions Table 34.1 summarizes the pdf, mean, and variance for certain continuous distributions.



Part VI.C.2

Table 34.1

pdf, mean, and variance for certain continuous distributions.

Distribution Normal

pdf (f(x)) 1 2p s

Exponential Weibull (threeparameter) Uniform

e

−

( x− m )

2

2s 2

le–lx

⎛ b ⎞ ⎛ x −g ⎞ ⎜⎝ q ⎟⎠ ⎜⎝ q ⎟⎠

b −1

e

⎛ x −g ⎞ −⎜ ⎝ q ⎠⎟

b

1 b−a

Mean

Variance

m

s2

1 l

1 l2

⎛1 ⎞ g +q Γ ⎜ + 1⎟ ⎝b ⎠

2 ⎡ ⎛2 ⎞ ⎛2 ⎞ ⎤ q 2 ⎢ Γ ⎜ + 1⎟ − Γ ⎜ + 1⎟ ⎥ ⎠ ⎝b ⎠ ⎥ ⎢⎣ ⎝ b ⎦

a+b 2

( b − a )2 12

2. DISCRETE DISTRIBUTIONS

Define and distinguish between these distributions: binomial, Poisson, hypergeometric, multinomial, etc. (Analyze) Body of Knowledge VI.C.2

Typical applications for discrete distributions in quality engineering include situations where the variable of interest is either the number of nonconformities or the number of nonconforming units in a sample. The variable represents a count and would have to be zero or a positive whole number. It should also be noted that for discrete distributions the term probability density function is not often used. Instead, the mathematical expression that describes the probability distribution for a discrete distribution is referred to as a probability mass function (pmf). The cumulative distribution function (cdf) also exists for discrete distributions.

Binomial The binomial distribution can be applied in situations where the experiment can result in only one of two possible outcomes, for example, good/bad, go/no-go, with/without, conforming/nonconforming, success/failure. In addition, the outcome on one run of the experiment (often referred to as a trial) does not affect the outcome on subsequent trials, that is, the trials are said to be independent. The outcomes are often referred to as a success or failure. Examples include number of heads on 50 flips of a fair coin or the number of manufactured parts that are out of specification. In one type of problem that is frequently encountered, the engineer needs to determine the probability of obtaining a certain number of nonconforming units in a sample.



The necessary conditions for a random variable to follow the binomial distribution are:

2. The n trials are independent. 3. Each trial results in one of two possible outcomes (success or failure). 4. The probability of a success is denoted by p; the probability of a failure is then 1 − p. The probability of a success is assumed constant trial to trial.

Part VI.C.2

1. There are a fixed number of observations or trials n.

Suppose an experiment consists of n independent trials. Let X represent the number of successes in n trials. Furthermore, let p be the probability of success in one trial. Then the probability of getting x successes in n trials is described by the pmf f ( x) = P(X = x) = n Cx p x (1 − p)n− x where x is the number of successes, with a probability of each success given by p. The number of failures is then n − x, where 1 – p is the probability of a failure. The combination nCx represents the number of ways x successes can occur in n trials, where

n

⎛ n⎞ n! Cx = ⎜ ⎟ = and n ! = n ( n − 1) ( n − 2 ) . . . 1 . ⎝ x ⎠ x !(n − x)!

EXAMPLE 34.8 Ten manufactured parts are randomly selected from a batch where it is believed that the percent nonconforming is 15 percent. It is important to determine the probability that exactly two out of the 10 manufactured parts will be nonconforming. We will define a success to be a nonconforming part. Let X represent the number of nonconforming parts. In this scenario, n = 10, x = 2, and p = 0.15. Then n

and

Cx =

10

⎛ 10 ⎞ 10 ! C2 = ⎜ ⎟ = = 45 ⎝ 2 ⎠ 2 !( 10 − 2)!

P( X = 2) =

10

C 2 ( 0 . 15)2 ( 1 − 0 . 15)10 − 2

= 45( 0 . 0225)( 0 . 85)8 = 0 . 2759 . The probability that a sample of size 10 will have exactly two nonconforming parts is approximately 0.2759.



Part VI.C.2

EXAMPLE 34.9 Consider the previous example. Suppose now we are interested in the probability of finding less than two nonconforming parts. If X represents the number of nonconforming parts, the probability that less than two nonconforming parts will be found in the sample is P(X < 2).

Solution: The value 2 is not included in this event of interest. So we can rewrite the probability equivalently as P(X < 2) = P(X ≤ 1) = P(X = 0) + P(X = 1). In this case, the binomial formula must be applied twice: P( X < 2) = P( X ≤ 1) = P( X = 0 ) + P( X = 1) =

10

C 0 ( 0 . 15)0 ( 1 − 0 . 15)10 − 0 + 10 C 1( 0 . 15)1( 1 − 0 . 15)10 − 1

= 1( 1)( 0 . 85)10 + 10( 0 . 15)( 0 . 85)9 = 0 . 1969 + 0 . 3474 = 0 . 5443 The probability that less than two parts out of 10 will be nonconforming is approximately 0.5443.

Expected Value and Variance. The expected value and variance for a random variable X that follows a binomial distribution with n trials and probability of success p are E(X ) = m = np V(X ) = s 2 = np(1 − p).

Poisson Distribution When observations take place over a continuum, such as time or space, then we do not have a finite series of discrete trials. For example, how often does a machine require adjustment, or how many nonconformities occur in a unit of fabric? The necessary conditions for a random variable to follow a Poisson distribution are: 1. The counts or occurrences are independent of each other. 2. The probability that a count occurs in an interval is the same for all intervals of that size or length. Let l be a parameter representing the mean number of counts over an interval. Let X represent the number of counts in the interval. Then the probability that x counts occur in an interval is described by the probability mass function



f ( x) = P(X = x) =

e−l l x , for x = 0, 1, 2 ,... x!

EXAMPLE 34.10

Part VI.C.2

where e = 2.71828 . . . .

The serious accident rate in a company is three per year. The probability there will be at most one serious accident during the next year is as follows. The probability of interest is P(X ≤ 1) = P(X = 0) + P(X = 1). P( X ≤ 1) = P( X = 0 ) + P( X = 1) e − 3 30 e − 3 31 + 0! 1! = 0 . 05 + 0 . 149 =

= 0 . 199 Therefore, the probability that at most one serious accident will occur during the next year is approximately 0.2.

Expected Value and Variance. The expected value and variance for a random variable X that follows a Poisson distribution with parameter l are E(X ) = m = l V(X ) = s 2 = l.

Hypergeometric Distribution When sampling from a finite population where independence is not assumed (for example, drawing cards without replacement from a deck of 52 cards, or selecting a sample of items from an isolated lot), then the probability changes with each observation. The hypergeometric distribution is used when items are drawn without replacement from a population of interest. Specifically, the items are not returned to the population before the next item (or items) is/are drawn. It is often used for samples taken from small populations. The items must fall into one of two categories such as conforming or nonconforming. Recall that the binomial distribution assumes either an infinite population or sampling with replacement (independent events). There can be a considerable difference when the population is small (results will be similar when the total population is large). Suppose we have a finite population of size N from which a sample of size n is drawn (without replacement). Furthermore, let A represent the number of nonconforming units in the population and let x represent the number of nonconforming units in the sample. The probability of obtaining x nonconforming items in a sample of size n for this situation is given by



Part VI.C.2


f ( x) =

⎛ A⎞ ⎛ N − A⎞ ⎜⎝ x ⎟⎠ ⎜⎝ n − x ⎟⎠ ⎛ N⎞ ⎜⎝ n ⎟⎠

where the combinations are: ⎛ A⎞ is the number of ways of choosing x nonconforming units ⎜⎝ x ⎟⎠ from A total possible nonconforming units. ⎛ N − A⎞ is the number of ways of choosing (n – x) conforming units ⎜⎝ n − x ⎟⎠ from a total of (N – A) conforming units in the population. ⎛ N ⎞ is the number of ways of choosing a sample of size n from ⎜⎝ n ⎟⎠ a population of size N.

EXAMPLE 34.11 The risk of implanting a biomedical device that may be nonconforming is important to quantify. Several assumptions must be made in order to obtain an accurate estimate of the risk, such as to whether or not devices have the same failure rate, how long the devices are stored before implantation, and so on. In addition, the number of nonconforming medical devices in a population of devices often has to be estimated using prior knowledge and/or previous data. For one of these biomedical devices manufactured by a local company, there has been one known failure after implantation within the last month. Based on prior information, it is assumed that out of 200 devices there are three that are nonconforming. If 30 medical devices are randomly selected out of the 200 devices, what is the probability that exactly one device will be nonconforming? For this situation, N = 200, n = 30, A = 3, and x = 1, and the probability of interest is P(X = 1): ⎛ 3⎞ ⎛ 200 − 3⎞ ⎜⎝ 1⎟⎠ ⎜⎝ 30 − 1 ⎟⎠ P( X = 1) = = 0.. 3281 ⎛ 200 ⎞ ⎜⎝ 30 ⎟⎠ The probability of selecting a sample of 30 devices and one is nonconforming is 0.3281.

Expected Value and Variance. The expected value and variance for the hypergeometric distribution with parameters N, A, n, x are E(X ) = m = np ⎛ N − n⎞ V(X ) = s 2 = np(1 − p) ⎜ ⎝ N − 1 ⎟⎠ where p = A/N.



Multinomial Distribution

Part VI.C.2

The multinomial distribution is used when an experiment consisting of n trials could result in more than two possible outcomes; the outcome is placed into one of several categories. For example, a randomly selected part could be classified as good, fair, or poor. Another example could be when a nonconforming part is found from a manufacturing process; it could be due to machine wear, temperature, or a problem with raw material.

Geometric and Pascal Distributions The geometric distribution involves independent trials that can result in one of only two possible outcomes, similar to the binomial distribution. However, for the geometric distribution the number of trials is not fixed. The random variable X represents the number of trials until the first success is obtained. An example would be determining the probability that x acceptable parts are produced before the first nonconforming part is generated. The geometric distribution is a special case of a more general distribution known as the Pascal distribution (also known as the negative binomial distribution). For the geometric distribution, sampling is terminated once the first success is obtained. For the Pascal distribution, sampling is terminated only after a fixed number of successes r has been obtained. Obviously, when r = 1 we have the special case of the geometric distribution.

Summary of Discrete Distributions The following rules can be applied for some of the key discrete distributions including the binomial, Poisson, and hypergeometric distributions: • Use with binary information (yes/no, conforming/nonconforming) and for nonconformities. • Use the hypergeometric when n > 5%N and when the sample is taken without replacement. • Use the binomial when the sample is taken with replacement or when n < 5%N and the sample is taken without replacement. • Use the Poisson when there can be more than one nonconformity per item or as an approximation for the binomial when n > 100 and np < 10. Table 34.2 summarizes the pmf, mean, and variance for certain discrete distributions.



Part VI.C.2

Table 34.2 pmf, mean, and variance for certain discrete distributions. Distribution Binomial Poisson Hypergeometric

pmf (f(x))

Mean

Variance

Cx p x (1 − p)n− x

np

np(1 – p)

e−l l x x!

l

l

⎛ A⎞ ⎛ N − A⎞ ⎜⎝ x ⎟⎠ ⎜⎝ n − x ⎟⎠

np

⎛ N − n⎞ np(1 − p) ⎜ ⎝ N − 1 ⎟⎠

n

⎛ N⎞ ⎜⎝ n ⎟⎠

GENERAL FORM OF EXPECTED VALUE AND VARIANCE For many of the distributions described in this chapter the specific expected value and variance were provided. In this section are formulas for expected value and variance for discrete and continuous distributions.

Continuous Distributions The expected value of a continuous random variable X with pdf f(x) is ∞

E(X ) = m X =

∫ xf (x)dx.

−∞

The variance of a continuous random variable X with pdf f(x) is ∞

V(X ) = s X2 =

∫ (x − m )

2

f ( x)dx.

X

−∞

Discrete Distributions The expected value of a discrete random variable X with pmf f(x) is E(X ) = m X = ∑ xf ( x) for all outcomes x from the distribution. Remember, f(x) describes the probability that x will occur. The variance of a discrete random variable X with pmf f(x) is V(X ) = s X2 = ∑ ( x − m ) f ( x). 2

As shown previously, the standard deviation for a random variable X, discrete or continuous, is the positive square root of the variance.



EXAMPLE 34.12

Number of medication errors X

0

1

2

3

Probability

0.90

0.07

0.02

0.01

Part VI.C.2

Suppose the number of medication errors for a patient at a hospital have the following probability mass function (written in table form):

The expected number of medication errors would be E( X ) = m X = ∑ xf ( x ) = 0( 0 . 90 ) + 1( 0 . 07 ) + 2( 0 . 02) + 3( 0 . 0 1) = 0 . 14 The variance of the number of medication errors would be V( X ) = ∑ ( x − m ) f ( x ) 2

= ( 0 − 0 . 14 )2 ( 0 . 90 ) + ( 1 − 0 . 14 )2 ( 0 . 07)) + ( 2 − 0 . 14 )2 ( 0 . 02) + ( 3 − 0 . 14 )2 ( 0 . 01) = 0 . 2204

CENTRAL LIMIT THEOREM REVISITED In Chapter 32 a very general statement about the central limit theorem was provided. With the introduction of the sampling distribution of the sample mean and a description of the normal distribution, more specific details of the central limit theorem are given here.

Definition Suppose X1, X2, . . . , Xn is a random sample taken from a distribution with mean m – and variance s 2. If n is sufficiently large, then the sample mean X follows approximately a normal distribution with mX– = m and variance sX–2 = s 2/n. Notice that the definition does not state that the underlying distribution from which the sample is drawn must be normally distributed. The true form of the distribution does not have to be normally distributed as long as the sample size is sufficiently large. There have been several recommended cutoffs for large n including n > 30 or n > 40. However, if the underlying distribution is normal, then the large n require– ment is not necessary. In this case, the sampling distribution of X will also follow a normal distribution with mean mX– = m and variance sX–2 = s 2/n.

Practical Use of the Central Limit Theorem An important application of the central limit theorem described here has been in calculating probabilities associated with the sample mean. In addition, it is important in statistical inference, statistical process control, process capability analysis, and so on, as we will see in the remaining chapters.



Part VI.C.2

Recall that if a random variable X follows a normally distributed random variable with mean m and variance s 2, then the random variable Z=

X−m s

will follow a standard normal distribution with m = 0 and s 2 = 1. By the central limit theorem stated above, Z=

X − mx X−m = sx s/ n

also follows a standard normal distribution with m = 0 and s 2 = 1. Using this result, we can find probabilities associated with the sample mean.

EXAMPLE 34.13 Recall the product-fill operation example given earlier in this chapter where the productfill operation produces net weights that are normally distributed with mean m = 8.06 ounces and standard deviation s = 0.37 ounces. a. What is the probability that a randomly selected container will weigh less than 7.08 ounces? b. What is the probability that a sample of nine randomly selected containers will have an average net weight less than 7.08 ounces?

Solution: Let X represent the weight of the containers. a. The probability of interest is P(X < 7.08). Transform X into the random variable Z using the relationship Z=

X−m , s

then from Appendix F find the appropriate probability. ⎡ X − m 7 . 08 − 8 . 06 ⎤ P ( X < 7 . 08 ) = P ⎢ < ⎥⎦ = P ( Z < − 2 . 6 5) = 0 . 0040 0 . 37 ⎣ s Therefore, the probability that a randomly selected container will have a net weight less than 7.08 is approximately 0.0040. (Note that this is the same answer we found previously.) – – b. The probability of interest here is P(X < 7.08). Transform X into the random variable Z using the relationship Z=

X−m : s/ n Continued



Continued

Therefore, it would be very unlikely that a random sample of nine such containers would have an average net weight less than 7.08 ounces.

Part VI.C.2

⎛ X − m 7 . 08 − 8 . 06 ⎞ P( X < 7 . 08 ) = P ⎜ < = P ( Z < − 7 . 95) = 0 ⎝s / n 0 . 37 / 9 ⎠⎟

Note: In the previous example, the value z = –7.95 is not given in the standard normal table in Appendix F. The table values only extend from –3.59 to 3.59. If the z value is not on the table, it does not mean that it is not a possible value. When the value of z is not on the table, the probability of interest will be practically zero or one, depending on the area of interest under the curve and the sign on the z-value. For example, the P(Z < –7.95) ≅ 0, P(Z > –7.95) ≅ 1, P(Z < 7.95) ≅ 1, P(Z > 7.95) ≅ 0.



Part VI.D.1

Chapter 35 D. Statistical Decision Making

1. POINT ESTIMATES AND CONFIDENCE INTERVALS

Define, describe, and assess the efficiency and bias of estimators. Calculate and interpret standard error, tolerance intervals, and confidence intervals. (Evaluate) Body of Knowledge VI.D.1

Point Estimation Suppose an estimate is needed for the average coating thickness of a population of 1000 circuit boards received from a supplier. Rather than measure all 1000 boards, randomly select a sample of 40 for measurement. Suppose that the average coating thickness of these 40 boards is 0.003 and the standard deviation of the 40 coating measurements is 0.005. The estimate for the average coating thickness on the entire lot of 1000 is then around 0.003. This value is called the point estimate. In this case the sample mean is a point estimator of the population mean (recall the definitions of population mean, sample mean, parameter, and statistic given in Chapter 33).

Efficiency, Bias, and Standard Error of Estimators To find information about a population of interest, it is important to be able to obtain information about the parameters that describe the population. Recall that parameters would include the population mean m and population variance s 2, for example. It is important to be able to estimate the parameters using information acquired from a sample taken from the population. A statistic, such as the sample mean, is used as a point estimator for a parameter, in this case the population mean (keep in mind that statistic refers to a value obtained from a sample and parameter is a characteristic of a population). The point estimator is said to be unbiased if the expected value of the point estimator is equal to the parameter that it is estimating. Consider the sample and 418


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 35: D. Statistical Decision Making 419

Part VI.D.1

population means. If a sample consists of n observations, X1, X2, . . . , Xn, taken – from a normal population, then the sample mean X is known to be an unbiased – estimator of the population mean m, that is, E(X) = m. It can also be shown that the sample variance s2 for the same situation (n observations taken from a normal distribution) is an unbiased estimator for the population variance s 2, that is E(s2) = s 2. However, the sample standard deviation s is not an unbiased estimator for s, yet the bias is often negligible for all but very small sample sizes. There is sometimes more than one possible unbiased point estimator for a parameter. One point estimator for a parameter is said to be more efficient than another if the variance of the point estimator is smaller than the variance of its competitor. As a simple example, consider a random sample of observations, X1, X2, . . . , Xn, taken from a population with mean m and variance s 2. The sample – mean X is one point estimator for the population mean m. However, any one observation Xi is also a possible point estimator for m. Recall from Chapter 33 that the – – variance of the sampling distribution of X is s 2/n (that is, V(X) = sX–2 = s 2/n) and the variance of a single observation from a population with variance s 2 is simply – – s 2 (that is, V(Xi) = s 2). Since V(X) < V(Xi), X is said to be a more efficient point estimator than Xi for m. The standard error (s.e.) of a statistic was briefly introduced in Chapter 33. In general, the standard error of a point estimator provides a measure of precision of the estimate and is simply the square root of the variance of the point estimator. For example, we know that the variance of the sampling distribution of the – point estimator X is s 2/n. The standard error is then s/√–n–. You sometimes see – this written as s.e. (X) = s/√–n–. When we calculate a statistic of interest, such as the sample mean, from real populations of interest we know that this estimate is not exactly equal to the true population mean. We could select 15 different samples of the same size n from the same population, calculate 15 sample means, and most if not all of them would be different. So which of the 15 would be the best estimate? Well, any one of the sample means would be an appropriate estimate of the population mean. As the sample size n increases, our estimates become more precise. If we were to use the entire population, our estimate would be perfect, but that is rarely found in practice. In nearly all cases, the sample means will be different even though they are drawn from the same population. The variability in the estimates needs to be quantified somehow.

Confidence Intervals In this section we will present: • Confidence intervals for a single population mean m • Confidence intervals for a single population variance s 2 • Confidence intervals for a single population standard deviation s • Confidence intervals for a single population proportion p. In the example given at the beginning of this chapter, is the population mean exactly 0.003? Almost surely not, due to sampling error. To capture information



Part VI.D.1


about a parameter, we need to quantify the variability in this estimate and then report it in a meaningful way. One type of estimation that can be used is called interval estimation. One of the most useful interval estimation approaches is constructing confidence intervals on the parameter of interest. In general, a confidence interval on a population parameter depends on: • A point estimate for the parameter of interest • An estimate of the standard error of the point estimate • A stated level of confidence, denoted by 1 – a (also called the confidence coefficient) • In some cases, an idea of the distribution of the underlying population, at least approximately For example, suppose I wish to construct a 95 percent confidence interval on a population mean m (complete details and discussion of confidence intervals will be presented later in this chapter). I would take a random sample from the population, calculate the necessary statistics (for example, sample mean and sample variance—if population variance is not known), and construct an interval on m using the sample information (and some other information). Suppose I found the 95 percent confidence interval on m to be 16 ≤ m ≤ 22. This is much more informative than just reporting that the sample mean was found to be x– = 19. General Form of the Confidence Interval. The general form of a 100(1 − a )% twosided confidence interval for any population parameter (we will denote it as q ) is L≤q≤U where L = lower endpoint of the confidence interval U = upper endpoint of the confidence interval q = parameter of interest, such as m or s 1 − a = confidence coefficient (level of confidence) Interpretation of the Confidence Interval. There are two general interpretations of the results from a confidence interval: the probabilistic interpretation and the practical interpretation. Probabilistic Interpretation. The probabilistic interpretation of the confidence level would be the proportion of all confidence intervals constructed on that parameter (under repeated sampling and identical conditions) that would contain the true parameter. For example, a 95 percent confidence interval on m would be interpreted as the percent of all confidence intervals constructed (under repeated sampling and identical conditions) that would contain the population mean m. Consider a normal population with known mean m = 25 from which 20 samples of size n = 100 are selected. Twenty 95 percent confidence intervals on m are constructed, with the following results: (24.4713, 25.6473)

(24.0548, 25.2308)



(23.9702, 25.1461)

(24.3994, 25.5754)

(24.8004, 25.9763)

(24.1861, 25.3620)

(24.4020, 25.5780)

(24.7750, 25.9510)

(23.8412, 25.0172)

(24.0617, 25.2377)

(24.4872, 25.6632)

(24.2578, 25.4337)

(24.4695, 25.6455)

(24.0038, 25.1798)

(25.0248, 26.2008)

(24.1231, 25.2991)

(24.4982, 25.6742)

(24.2900, 25.4659)

(24.1859, 25.3619)

Part VI.D.1

(24.5202, 25.6962)

What percent of these confidence intervals contain the true population mean m = 25? In this scenario, 19 out of 20 (or 95 percent) of the confidence intervals constructed contain the true population mean m = 25. That also means that five percent of all intervals constructed would not contain the true population mean. Practical Interpretation. Since the true parameter value, such as the population mean m, is usually unknown in practice, the probabilistic interpretation may not be very useful. Additionally, we often only have one sample of data, not 20. The practical interpretation of a confidence interval constructed in this situation would be a statement of degree of belief that the confidence interval contains the true m. For example, we are 95 percent confident that the 95 percent confidence interval will contain the true population mean m. It should be noted that the practical interpretation should never be misconstrued as saying that the “probability that the confidence interval contains the true value m is 0.95.” Remember, the true value of the population mean m exists but is unknown. Therefore, when we construct a confidence interval on m, then either the interval contains the true value of m (probability of 1) or not (probability of 0). Next, confidence intervals involving single samples will be covered. Confidence Intervals for a Single Population Mean l. In this section, the presentation of confidence intervals on a population mean will be divided into two cases: 1. Confidence intervals on m when the population variance s 2 is assumed known 2. Confidence intervals on m when the population variance s 2 is assumed unknown Case 1: Population Variance r 2 Is Assumed Known. Let X1, X2, . . . , Xn represent a random sample taken from a normal distribution with known variance s 2 but unknown population mean m. From Chapter 34, we know that the sampling – distribution of X is normally distributed with mean m and variance s 2/n. And Z=

X−m s/ n



Part VI.D.1

follows a standard normal distribution. It can be shown that a general 100(1 – a )% confidence interval on m is given by x − za /2

s n

≤ m ≤ x + za /2

s n

where x– = sample mean n = sample size s n

= standard error of the mean

z a/2 = multiple of the standard error of the mean, which determines the width of the interval. It is a direct result of the level of confidence 1 − a and is found using the standard normal distribution. The subscript a/2 is the area to the right of the number z a/2 under the standard normal curve.

EXAMPLE 35.1 A manufacturing process has been running in control for some length of time. The quality characteristic of interest is diameter of the manufactured part (measured in mm). It is believed there may have been a shift in the process mean due to the change of a raw material. A sample of 25 items is randomly selected from the process, measured, and the sample average found to be 103 mm. The in-control process average has been 102 mm, which is nominal. The standard deviation during the time the process was believed to be in control was three mm. The customer wants to construct a 95 percent confidence interval on the true process mean to determine if the process has shifted away from nominal.

Solution: The necessary information is: • x– = 103. • s = 3. • n = 25. • 1 − a = 0.95, and solving for a , we get a = 0.05. This value is necessary to find the multiple z a/2 from the standard normal table. From the standard normal table in Appendix F with a = 0.05, z a/2 = z0.05/2 = z0.025 = 1.96. The resulting 95 percent confidence interval is then 3 3 ≤ m ≤ 103 + 1 . 96 25 25 103 − 1 . 176 ≤ m ≤ 103 + 1 . 1 76

103 − 1 . 96

101 . 824 ≤ m ≤ 104 . 176 With a high degree of confidence, we can say 101.824 mm ≤ m ≤ 104.176 mm. Since the nominal value of 102 mm is contained in this interval, there is insufficient evidence to conclude at the 95 percent level of confidence that the process mean has not shifted.



EXAMPLE 35.2

Solution: With all other information held constant, the only change is in the multiple Za/2 . In this case 1 – a = 0.90, so a = 0.10 and z a/2 = z0.10/2 = z0.05 = 1.645. The 90 percent confidence interval is then

Part VI.D.1

Reconsider the previous example. What conclusions could be reached if the level of confidence were changed to 0.90?

3 3 ≤ m ≤ 103 + 1 . 645 25 25 103 − 0 . 987 ≤ m ≤ 103 + 0 . 987

103 − 1 . 645

102 . 013 ≤ m ≤ 103 . 987 . With a high degree of confidence, we can say 102.013 mm ≤ m ≤ 103.987 mm. Since the nominal value of 102 mm lies outside this interval, there is sufficient evidence to conclude that the process mean has shifted. In fact, we would conclude based on this data that the process mean has shifted upward. Notice that the nominal value of 102 mm lies just outside the lower bound of the confidence interval. Whether or not this shift would result in corrective action should be determined by personnel familiar with the manufacturing process.

Examine the 95 percent and 90 percent confidence intervals for the previous examples. Decreasing the confidence level from 95 percent to 90 percent decreased the length of the confidence interval. Decreasing (increasing) the level of confidence—while all other quantities remain constant—will always result in a decrease (increase) in the length of the confidence interval. Determining the Sample Size. There are many situations where the practitioner would like to know how large a sample is needed in order to estimate a population mean with some level of precision. This information can be obtained using the 100(1 – a )% confidence interval and making some reasonable assumptions. The length of the confidence interval, x − za /2

s n

≤ m ≤ x + za /2

s n

,

provides a measure of precision of estimation. The margin of error in the confidence interval formula is za /2

s n

.

This quantity provides information about the accuracy of the confidence interval. That is, when x– is used as the estimate for m, the error E= x−m



Part VI.D.1

will be at most za /2

s n

.

So, if E = za /2

s n

is an upper bound on the amount of error the practitioner is willing to live with, an appropriate sample size can be determined. That is, for a given level of confidence, an assumed value of s, and a margin of error E one is willing to live with, a minimum sample size can be found using the relationship E = Za /2

s n

and solving for n: ⎡s Z ⎤ n ≥ ⎢ a /2 ⎥ ⎣ E ⎦

2

If the value of n is not an integer, round up to the nearest integer.

EXAMPLE 35.3 Suppose the turnaround time for basic blood analysis for the emergency room at a local hospital is of interest. A goal is to be able to estimate the true average turnaround time m . Specifically, it is of interest to obtain an estimate that is within three minutes of the true average turnaround time with 95 percent confidence. Based on prior information it is assumed that s = 8 min. How many turnaround times should be obtained in order to estimate the true average turnaround time and meet the requirements stated? That is, what is n?

Solution: First, note that the problem states that the estimate is “within three minutes of the true average. . . . ” This implies that we will allow the estimate to be at most three minutes less than the true average or at most three minutes greater than the true average. In this case, the margin of error we are willing to live with is E = 3 min. The level of confidence is 95 percent so 1 – a = 0.95, a = 0.05, and z a/2 = z0.025 = 1.96. With s = 8 min, the minimum sample size needed is 2

2

2 ⎡ s Z ⎤ ⎡ 8( 1 . 96 ) ⎤ n ≥ ⎢ a /2 ⎥ = ⎢ = [ 5 . 227 ] = 27.. 32 . ⎣ E ⎦ ⎣ 3 ⎥⎦

Therefore, to meet the requirements of 95 percent confidence, a margin of error of no more than three minutes, and s = 8 min, the minimum number of times to obtain would be n = 28.



Part VI.D.1

Case 2: Population Variance r 2 Is Assumed Unknown. Suppose the population of interest is normally distributed with unknown mean m and unknown variance s 2. Let X1, X2, . . . , Xn be a random sample taken from the population, and let the sample mean and variance be denoted by x– and s2. When s is unknown, it can be estimated by s. Furthermore, if the sample size n is small, the standard normal distribution is no longer an appropriate distribution for the sample mean. In its place we will use Student’s t distribution (recall the t distribution from Chapter 34). Definition. Let X1, X2, . . . , Xn be a random sample taken from a normal population, and let the sample mean and variance be denoted by x– and s2. The random variable: T=

X−m s/ n

follows a t distribution with k = n – 1 degrees of freedom. It should be noted that the t distribution is defined by its degrees of freedom, which are directly related to the sample size n. As the degrees of freedom go to infinity, the t distribution approaches the standard normal distribution. The degrees of freedom are of particular interest. The degrees of freedom for T is n – 1. This is a direct result of using s2 as an estimate for s 2. Recall that the sample variance is based on the deviations x1 – x–, x2 – x–, . . . , xn – x–, and that n

∑(x

i

− x ) = 0.

∑(x

− x ) = 0,

i=1

Since n

i

i=1

this implies that if we specify n – 1 of these deviations, the nth deviation is automatically determined (it can not vary freely). That is, only n – 1 of these deviations can vary freely. The degrees of freedom for the t distribution represent the number of deviations that can vary freely. It can be shown that the 100(1 – a )% confidence interval on the population mean m is given by x − ta /2 , k

s n

≤ m ≤ x + ta /2 , k

s n

where ta/2,k is the multiple of the standard error of the mean (s/√–n–) and determines the width of the interval. It is a direct result of the level of confidence 1 − a and is found using Student’s t distribution. The subscript a/2 is the area to the right of the number ta/2,k that we need, and k = n – 1 is the degrees of freedom for a particular problem. Probabilities and values for the t distribution are given in Appendix P.



Part VI.D.1

EXAMPLE 35.4 A study on the effect the type of joystick has on powered wheelchair driving performance was conducted. In the study, one type of joystick used was a position-sensing joystick. Suppose eight subjects are asked to use a position-sensing joystick, and the time to complete a predetermined task is recorded (in minutes). The results are: 30.7, 31.2, 26.1, 29.4, 34.6, 26.8, 33.1, 25.5 Assume that time to complete the task is well approximated by a normal distribution. The investigators would like to construct a 95 percent confidence interval on the true time to complete the task using the position-sensing joystick.

Solution: First, we need to calculate the values x– and s: n

x=

∑x i=1

n

i

= 29 . 675

n

s=

∑(x i=1

i

− x )2

n−1

= 3 . 3363

For a level of confidence of 95 percent, 1 – a = 0.95, and solving for a , we get a = 0.05. The degrees of freedom for this problem is k = n – 1 = 7. These values are necessary to find the multiple t a/2,k from the t distribution. From the t distribution table in Appendix P, with a = 0.05, t a/2,k = t0.025,7 = 2.306. The resulting 95 percent two-sided confidence interval is then 3 . 3363 3 . 3363 ≤ m ≤ 29 . 675 + 2 . 365 8 8 = 29 . 67 5 − 2 . 790 ≤ m ≤ 29 . 675 + 2 . 790 29 . 675 − 2 . 365

= 26 . 89 ≤ m ≤ 32 . 47 . With a high degree of confidence we believe the true time to perform the task using the position-sensing joystick lies between 26.89 minutes and 32.47 minutes.

In the previous example the sample size was small, n = 8. In cases where the sample size is small (cutoffs have often been n < 30 or n < 40), the population is known to be normally distributed, and the population variance is unknown, the t distribution is an appropriate distribution. In cases where the sample size is sufficiently large, the population is known to be normally distributed, and the population variance is unknown, the standard normal distribution or the t distribution could be used. Again, the t distribution and standard normal distribution are approximately of the same form as the sample size n goes to infinity. Large-Sample Confidence Interval on l (r 2 Unknown). Now in addition to the population mean and population variance being unknown, assume that the



x − zs /2

s n

≤ m ≤ x + zs /2

s n

can be used. However, if the sample size is small and the underlying distribution is decidedly nonnormally distributed, then nonparametric methods should be used to construct the confidence interval.

Part VI.D.1

underlying distribution is not necessarily normally distributed. In this situation, if the sample size is large, then the central limit theorem applies and the 100(1 – a )% confidence interval

Statistical Tolerance Intervals. Consider a population of manufactured steel rods. Suppose the diameters of these steel rods follow a normal distribution with mean m = 25 mm and standard deviation s = 4 mm. Then the interval (m – 1.96s, m + 1.96s ) = (17.16, 32.84) would capture the diameters of 95 percent of the steel rods. This is a result of the fact that 95 percent of the area under the normal curve would lie between –1.96 and 1.96. In many situations, m and s are unknown and are estimated using x– and s. The interval (x– – 1.96s, x– + 1.96s) may not actually contain 95 percent of the values in the population (since there is variability in the estimates in this case, there is no guarantee that the tolerance interval will contain 95 percent of the values). The solution to the problem is to replace 1.96 with some value that will make the resulting interval contain 95 percent of the values with a high level of confidence. This interval is referred to as a tolerance interval. Definition. Suppose we wish to capture at least t % of the values in a normal distribution with a 100(1 – a )% level of confidence. The appropriate two-sided tolerance interval is x– – Ks, x– + Ks where K is a tolerance interval factor found in Appendix D. Only selected values of K are given in the table, in particular for 99 percent of the population for 90 percent, 95 percent, and 99 percent levels of confidence.

EXAMPLE 35.5 Suppose the time to complete a task is of interest. A sample of n = 10 times are collected and it is found that x– = 31.50 and s = 2.764. Time to complete the task is assumed to be normally distributed. Suppose we want to find a tolerance interval for time that includes 99 percent of the values in the population with 95 percent confidence. From Table D with n = 10 and confidence level of 0.95 we find K = 4.433. The resulting tolerance interval is (x– – Ks, x– + Ks) = (31.50 – 4.433(2.764), 31.50 + 4.433(2.764)) = (19.25, 43.75) We can be 95 percent confident that at least 99 percent of all times to complete the task are between 19.25 minutes and 43.75 minutes.



Part VI.D.1


Confidence Intervals on a Population Variance r 2 . As stated previously, the sample variance s2 can be used as the point estimate for the population variance s 2. The sample standard deviation s can be used as the point estimate for s. Suppose instead of relying on a point estimate of s 2, we can construct a confidence interval. Assume a normally distributed population from which a random sample X1, X2, . . . , Xn is selected. The 100(1 − a )% two-sided confidence interval on the population variance s 2 is given by (n − 1)s 2 (n − 1)s 2 2 < < s c a2/2 , k c 12−a /2 , k 2 2 where c a/2,k and c 1–a/2,k are values obtained from the chi-square distribution with a/2 and (1 − a/2) being the areas under the chi-square curve and to the right of the value of interest to be put into the confidence interval. Again, k = n – 1 is the degrees of freedom. Table values of the chi-square distribution are given in Appendix K. (Recall the chi-square distribution from Chapter 34.)

EXAMPLE 35.6 A heart rate stress test was administered to 10 men. The heart rates (in beats per minute [bpm]) were recorded. The average heart rate was found to be x– = 100 bpm with a standard deviation of s = 16.2 bpm. A confidence interval was constructed on the mean heart rate and it is now desired to construct a 95 percent confidence interval on the variance in heart rate.

Solution: The necessary information is: s2 = (16.2 bpm)2 = 262.44 bpm2 k=n–1=9 1 − a = 0.95, therefore a = 0.05 and the corresponding chi-square values obtained from a chi-square table are c a2 / 2 ,k = c 02. 05/ 2 ,9 = c 02. 025 ,9 = 19 . 023 c 12−α / 2 ,k = c 12− 0 . 05/ 2 ,9 = c 02. 975 ,9 = 2 . 700 . The resulting 95 percent two-sided confidence interval on the population variance is ( 10 − 1)262 . 44 ( 10 − 1)262 . 44


(n − 1)s 2 (n − 1)s 2
Part VI.D.1

Confidence Intervals on a Population Standard Deviation r. An approximate confidence interval on the population standard deviation s can easily be found by taking the square of the bounds on the population variance. An approximate 100(1 – a )% confidence interval on s is given by

EXAMPLE 35.7 Reconsider the previous example. A 95 percent confidence interval on the standard deviation of heart rate is 124 . 16 < s 2 < 874 . 80 11 . 14 < s < 29 . 58 . We are highly confident the true population standard deviation will lie between 11.14 bpm and 29.58 bpm.

Confidence Intervals on a Population Proportion p. The population proportion p often represents the fraction nonconforming or fraction of defective items in a population of items. Since it is often impossible or impractical to find the exact value of p, a point estimate is used to estimate the true proportion. A sample proportion denoted pˆ will be used as a point estimate for p. The sample proportion is calculated using pˆ =

x n

where x is the number of successes out of n trials. If n is sufficiently large, then it can be shown that the random variable Z=

pˆ − p p(1 − p) n

follows a standard normal distribution. This relationship allows for the development of a confidence interval on the true population proportion of interest. A 100(1 – a )% confidence interval on a population proportion p is given by pˆ − zs /2

pˆ (1 − pˆ ) pˆ (1 − pˆ ) . ≤ p ≤ pˆ + zs /2 n n



Part VI.D.2

EXAMPLE 35.8 Billing statements for 1000 patients discharged from a particular hospital were randomly selected for errors. Out of the 1000 billing statements, 102 were found to contain errors. Using this information, it is desired to construct a 99 percent confidence interval on the true proportion of billing statements with errors.

Solution: In this problem, a “success” is a billing statement with errors. Therefore, X = 102 and n = 1000. The sample proportion is then pˆ =

102 = 0 . 102 1000

and provides our best estimate for the true proportion of billing statements with errors. The corresponding 99 percent confidence interval is pˆ − za / 2

pˆ ( 1 − pˆ ) pˆ ( 1 − pˆ ) ≤ p ≤ pˆ + za / 2 n n

0 . 102( 1 − 0 . 10 0.. 102( 1 − 0 . 102) ≤ p ≤ 0 . 102 + 2 . 576 1000 1 0 00 0 . 077 ≤ p ≤ 0 . 127 0 . 102 − 2 . 576

With a high degree of confidence, we believe the true proportion of billing statements that contain errors would be between 7.7 percent and 12.7 percent.

Note: The procedure outlined here for the population proportion assumes that the normal distribution is a good approximation to the binomial. That is, the parameter of interest is a proportion that is well modeled by the binomial distribution, but if the sample size is large enough, the normal distribution is a good approximation to the binomial. Recommendations have been that np ≥ 5 and np(1 – p) ≥ 5. However, if the sample size is small, the confidence interval given previously may not be a good approximation. If the sample size is small, then the binomial distribution should be used to find the multiple of the standard error in the confidence interval instead of the standard normal distribution. Agresti and Coull (1998) present an alternative form of a confidence interval for small n. (In addition see Devore [2007].)

2. HYPOTHESIS TESTING Define, interpret, and apply hypothesis tests for means, variances, and proportions. Apply and interpret the concepts of significance level, power, type I and type II errors. Define and distinguish between statistical and practical significance. (Evaluate) Body of Knowledge VI.D.2



1. State the null hypotheses (H0) and alternative hypothesis (Ha). 2. Choose the level of significance a.

Part VI.D.2

The hypothesis test, another tool used in inferential statistics, is closely related to confidence intervals, a relationship that is illustrated in this section. Textbooks tend to treat hypothesis tests as somewhat more formal procedures. Many list seven or eight steps to be followed for each type of test. Although not all books agree on the steps themselves, this list is fairly generic:

3. Determine the rejection region for the statistic of interest. 4. Calculate the test statistic. 5. Decide whether the null hypothesis should be rejected. 6. State the conclusion in terms of the original problem. General descriptions of each step are provided. 1. Write the assumption that is claimed to be true in a null hypothesis. The null hypothesis is denoted by H0. This is the statement that we are assuming to be true and we are trying to find evidence against. For example, from past experience the average time to complete a task is five minutes, and we would like to test this claim. The null hypothesis would be H0: m = 5. State the alternative hypothesis. The notation for the alternative hypothesis is usually Ha. If the null hypothesis is rejected, then an alternative must be provided. The alternative hypothesis contains the statement that we would eventually like to support (we can support the alternative only if the null hypothesis is rejected). For example, suppose we wish to show that the average time to complete a task is actually longer than five minutes. The alternative hypothesis would be Ha: m > 5. 2. Choose the level of significance for the test. The significance level (denoted a ) is the probability of making the mistake of rejecting the null hypothesis when it is in fact true. This type of mistake is called a type I error (as opposed to a type II error, which is the mistake of not rejecting the null hypothesis when it is in fact false). The probability of committing a type I error should be small (that is, a ≤ 0.10) and should be set before performing the test. The significance level a is the same a used in the level of confidence in constructing confidence intervals (recall that 1 – a is the level of confidence, so a is the level of significance). Choosing the level of significance is sometimes an economic decision. It is established based on how expensive a particular mistake may be (this expense is not necessarily monetary, but could also include personal injury or loss of life). 3. Determine the rejection region. The rejection region consists of all those values of the test statistic for which the null hypothesis would be rejected. The rejection region is determined by the stated level of significance and the alternative hypothesis. Critical values are those values that determine the rejection region (they are cutoff values for the test statistic—to be discussed next). 4. Calculate the test statistic. The test statistic is simply a statistic (such as the sample mean or sample variance) that has been transformed in order to compare this value to some standard (critical values). An example of a test statistic would – be a sample mean X transformed into a Z-score. The Z-score would be used to determine if the null hypothesis could be rejected.



Part VI.D.2


5. Compare the test statistic to the critical value. If the test statistic falls within the rejection region, then the null hypothesis would be rejected. Large test statistic values (in absolute value) will provide evidence against the null hypothesis. It is important to note that rejecting the null hypothesis is a strong claim. It takes significant evidence to reject the claim associated with H0. As a result of using a small level of significance, rejecting the null hypothesis is a strong claim. Failing to reject the null hypothesis is a weak claim. That is, by failing to reject the null hypothesis, we can not say that the claim is true, just that we did not have sufficient evidence to reject the claim. 6. Once it has been determined whether or not the null hypothesis can be rejected, the results are written in terms of the problem statement. The following hypothesis tests will be presented in this section: • Hypothesis tests for a single population mean m • Hypothesis tests for a single population variance s 2 • Hypothesis tests for a single population standard deviation s • Hypothesis tests for a single population proportion p

Hypothesis Tests for a Single Population Mean l Let m0 be a real value that is hypothesized or assumed to be the true value of the population mean m. There are three types of hypothesis tests: H0: m ≤ m0

Ha: m > m0

(Right-tailed test)

H0: m ≥ m0

Ha: m < m0

(Left-tailed test)

H0: m = m0

Ha: m ≠ m0

(Two-tailed test)

As with confidence intervals, there are two cases under study: 1. Hypothesis tests on m when the population variance s 2 is assumed known 2. Hypothesis tests on m when the population variance s 2 is unknown Case 1. Hypothesis Tests on l When the Population Variance r 2 Is Assumed Known. Assume that the underlying population of interest is normally distributed with known population variance s 2. Let X1, X2, . . . , Xn be a random sample – from the population, and let X represent the sample mean. An appropriate test statistic for this situation is z0 =

X − m0 s/ n

.

The rejection region depends on the alternative hypothesis and level of significance a :



Reject H0 if z0 > za

Ha : m < m 0

z0 < –z a

Ha : m ≠ m 0

z0 < –z a/2 or z0 > z a/2

Part VI.D.2

Alternative hypothesis Ha: m > m 0

EXAMPLE 35.9 A vendor claims that the average weight of a shipment of parts is 1.84 kg. The customer randomly chooses 64 parts and finds that the sample has an average weight of 1.88 kg. Suppose that the standard deviation of the population is known to be 0.3 kg. Using a level of significance of 0.05, we want to test the hypothesis that the true average weight of the shipment is 1.84 kg. We assume that the weights are normally distributed.

Solution: Since the population standard deviation is known, the standard normal distribution is used. In addition, since the problem statement gives no indication as to whether the true mean weight is less than or greater than 1.84 kg, a two-sided hypothesis test is appropriate. The steps are as follows: 1. The null and alternative hypotheses are H 0 : m = 1.84

Ha: m ≠ 1.84.

2. The level of significance is a = 0.05. 3. There are two areas for the rejection region since we have a two-tailed test. The critical values are found from a standard normal table with a = 0.05: z a/2 = z0.05/2 = z0.025 = 1.96 and –z a/2 = –1.96. Therefore, reject the null hypothesis if the test statistic is greater than 1.96 or less than −1.96. 4. The test statistic is calculated to be: z0 =

x − m 0 1 . 88 − 1 . 84 = = 1 . 07 s / n 0 . 30 / 64

(Note: This test statistic says the following: the sample mean is roughly 1.07 standard deviations away from the population mean. Is this a large difference? The next step tells us how large is large.) 5. Comparing 1.07 to the critical values in step 4, we see that −1.96 < 1.07 < 1.96. Since the test statistic, 1.07, does not fall into the rejection region, we can not reject the null hypothesis at the 0.05 level of significance. We failed to reject the null hypothesis (weak claim). 6. There is insufficient evidence to conclude that the true mean weight of the shipment is not 1.84.



Part VI.D.2


Case 2. Hypothesis Tests on l When the Population Variance r 2 Is Unknown. Assume that the underlying population of interest is normally distributed with unknown population variance s 2. Let X1, X2, . . . , Xn be a random sample from – the population and let X represent the sample mean. If the sample size is small, an appropriate test statistic for this situation is t0 =

X − m0 s/ n

,

which follows a t distribution with k = n – 1. The rejection region depends on the alternative hypothesis and level of significance a : Alternative hypothesis

Reject H0 if

Ha: m > m 0

t 0 > t a,k

Ha : m < m 0

t 0 < –t a,k

Ha : m ≠ m 0

t 0 < –t a/2,k or t 0 > t a/2,k

It is important to note here that the fact that the null hypothesis is not rejected does not mean it is true. It only indicates that with this sample of data we could not find evidence against the null hypothesis. If a second sample of data is taken, it is possible that the null hypothesis could be rejected.

EXAMPLE 35.10 A cutoff saw has been producing parts with a mean length of 4.125 mm. A new blade is installed and we want to know whether the mean has decreased. We select a random sample of 20 parts, measure the length of each part, and find the mean length to be 4.120 mm and the sample standard deviation to be 0.008 mm. Assume that the population is normally distributed. Using a significance level of 0.10, determine whether the mean length has decreased. Since the population standard deviation is unknown, the t-test will be used.

Solution: 1. The null and alternative hypotheses are H 0 : m = 4.125

Ha: m < 4.125.

2. The level of significance is a = 0.10. 3. There is only one rejection region since this is a left-tailed test (again, this is based on the alternative hypothesis). The critical value is found from the t distribution table in Appendix P with a = 0.10: t a,k = t 0.10,19 = 1.328. Therefore, reject the null hypothesis if the test statistic is less than –1.328 (since the alternative hypothesis is “less than”). 4. The test statistic is calculated to be: t0 =

x − m 0 4 . 120 − 4 . 125 = = − 2 . 80 s/ n 0 . 008 / 20 Continued



Continued

Part VI.D.2

5. Comparing –2.80 to the critical value in step 4, we see that –2.80 < –1.328. Since the test statistic falls into the rejection region, we can reject the null hypothesis at the 0.10 level of significance (strong claim). 6. There is sufficient evidence to indicate that the average length of the part has decreased.

Table 35.1

Summary of situations outlined for testing the population mean.

Assumption

Distribution

s 2 known, population normally distributed

Standard normal distribution

s 2 unknown and n small (n ≤ 30), population normally distributed

t distribution

s 2 unknown and n large (often n > 30), population not necessarily normal

Standard normal distribution or t distribution*

Test statistic z0 =

t0 = z0 = t0 =

x − m0 s/ n x − m0 s/ n x − m0 s/ n

or

x − m0 s/ n

* Recall that as n goes to infinity (n → ∞), the form of the t distribution becomes indistinguishable from the standard normal distribution.

A summary of the situations outlined for testing the population mean are shown in Table 35.1 (assume the underlying population is normally distributed).

Hypothesis Tests on a Population Variance r 2 The hypothesis testing procedure can be applied in the case of testing a standard or hypothesized value of the population variance. For example, we may be interested in testing the claims that a particular population variance is eight. The null hypothesis is H0: s 2 = 8. Assume that the underlying distribution is normal. Let s 02 be a real value that is hypothesized or assumed to be the true value of the population variance s 2. There are three types of hypothesis tests on a population variance: H0: s 2 ≤ s 02

Ha: s 2 > s 02

(Right-tailed test)

H0: s 2 ≥ s 02

Ha: s 2 < s 02

(Left-tailed test)

H0: s 2 = s 02

Ha: s 2 ≠ s 02

(Two-tailed test)

The chi-square distribution introduced in Chapter 34 is an appropriate distribution for testing a population variance. The test statistic is given by



Part VI.D.2

c 02 =

(n − 1)s 2 . s 02

The test statistic follows a chi-square distribution with k = n – 1 degrees of freedom, where: • s2 is the sample variance for a sample taken from the population of interest. • s 02 is the hypothesized value of the variance. • n = sample size chosen from the population of interest. The test of hypothesis steps outlined earlier also apply to this situation. Alternative hypothesis

Reject H0 if

Ha: s 2 > s 02

2 c 02 > c a,k

Ha : s < s

2 c 02 < c 1– a,k

2

2 0

Ha: s 2 ≠ s 02

2 2 2 c 02 < c 1– a /2,k or c 0 > c a /2,k

The critical values for the chi-square distribution can be found in Appendix K.

EXAMPLE 35.11 A process has been running for some time with a variance of 6.25 for a critical dimension. In an effort to improve throughput, a methods engineer increases the drive motor speed. A sample of 13 items is randomly selected from the new process. The variance of the critical dimension in this sample is found to be 6.82. Is there sufficient evidence to conclude that the true process variance has increased? Use a = 0.05. Assume that the critical dimension follows a normal distribution.

Solution: 1. The null and alternative hypotheses are H 0 : s 2 = 6.25

H1: s 2 > 6.25.

2. The level of significance is a = 0.05. 3. Since this is a right-tailed test, we would reject the null hypothesis if the test 2 2 = c 0.05,12 = 21.026. statistic is greater than c a,k 4. The test statistic is calculated to be c 02 =

(n − 1)s2 ( 13 − 1)6 . 82 = = 13 . 1 . s 02 6 . 25

5. Since 13.1 < 21.026, we would not reject the null hypothesis (weak claim). 6. There is insufficient evidence to conclude that the variance for this critical dimension has increased significantly at the 0.05 level of significance.



Hypothesis Testing on a Population Proportion p

H0: p ≤ p0

Ha: p > p0

(Right-tailed test)

H0: p ≥ p0

Ha: p < p0

(Left-tailed test)

H0: p = p0

Ha: p ≠ p0

(Two-tailed test)

Part VI.D.2

The hypothesis testing procedure is applied in this case testing a standard or hypothesized value of a population proportion. For example, we may be interested in testing the claim that a particular population proportion is 0.50. The null hypothesis is then H0: p = 0.50. Assume that the underlying distribution is normal. Let p0 be a real value that is hypothesized or assumed to be the true value of the population proportion. There are three types of hypothesis tests on a population proportion:

It should be noted that you will often see the null hypothesis written simply as H0: p = p0 for each of the three cases. Either notation is acceptable. For sufficiently large sample sizes, the normal approximation to the binomial distribution is valid and the test statistic z0 =

pˆ − p0 p0 (1 − p0 ) n

follows a standard normal distribution where pˆ = sample proportion for a sample taken from the population of interest. By definition: pˆ =

x n

x represents the number of successes out of a sample size of (number of trials) n p0 = hypothesized value of the population proportion. If the sample sizes are relatively small, then the appropriate hypothesis test to use is based directly on the binomial distribution. See Devore (2007) for more details on small-sample tests. The rejection region depends on the alternative hypothesis and level of significance a :

Alternative hypothesis

Reject H0 if

Ha: p > p 0

z0 > za

Ha : p < p 0

z0 < –z a

Ha : p ≠ p 0

z0 < –z a/2 or z0 > z a/2



Part VI.D.2

EXAMPLE 35.12 Billing statements for discharged patients from a particular hospital sometimes contain errors. It is believed that the percentage of billing statements that contain errors is 15 percent. Out of 1000 billing statements that are randomly selected from the population, 102 were found to contain errors. Based on this information, can we conclude that the proportion of billing statements that contain errors is actually less than 15 percent? Use a 10 percent level of significance.

Solution: For this problem: • p = the true proportion of billing statements with errors. • x = number of statements with errors, so x = 102. • n = sample size, so n = 1000. • The sample proportion is then pˆ =

x 102 = = 0 . 102 . n 1000

1. The null and alternative hypotheses are H 0 : p = 0.15 and Ha: p < 0.15. 2. The level of significance is a = 0.10 3. Since this is a left-tailed test, reject the null hypothesis if the test statistic is less than –z a = –z0.10 = –1.28. 4. The test statistic is z0 =

pˆ − p0 = p0 ( 1 − p0 ) n

0 . 102 − 0 . 15 = − 4 . 25 . 0 . 15( 1 − 0 . 15) 100 0

5. Since –4.25 < –1.28, we would reject the null hypothesis in favor of the alternative at the 0.10 level of significance (strong claim). 6. There is sufficient evidence to conclude that the true percentage of statement errors is less than 15 percent.

Hypothesis Tests and Confidence Intervals for Two Population Means l 1, l 2 There are many important cases that involve comparing two populations of interest, such as comparing two processes, two vendors, and so on. Of interest here is testing the difference in two population means, m1 – m2. One test in particular is that of no difference between the two populations m1 – m2 = 0. Either the standard normal distribution or Student’s t distribution can be used. If the population variances are known, use the standard normal distribution. If the population variances are unknown and the sample sizes are relatively small, use Student’s t distribution.



The basic assumptions are as follows:

• Y1, Y2, . . . , Yn2 is a random sample from a population with mean m2 and variance s 22. • The two samples are independent of each other.

Part VI.D.2

• X1, X2, . . . , Xn1 is a random sample from a population with mean m1 and variance s 12.

A good point estimator for m1 – m2 would be the difference between the two sample – – means, X – Y. The expected value and variance for this point estimator are

(

)

E X − Y = m1 − m 2 and

(

)

V X − Y = s X2 −Y =

s 12 s 22 + . n1 n2

– – If we assume that populations are normally distributed, then X – Y will also be normally distributed. Therefore, the random variable Z=

(X − Y ) − ( m

1

− m2 )

s 12 s 22 + n1 n2

follows a standard normal distribution. As a result, hypothesis tests and confidence intervals can be constructed on the parameter m1 – m2.

Hypothesis Test on l 1 – l 2 , with Population Variances Known Let ∆0 be a real value that represents the difference between m1 and m2 that is of interest to be tested. Specifically, the null hypothesis would be H0: m1 – m2 = ∆0. For the test of no difference, ∆0 = 0. The hypothesis tests are as follows: Null hypothesis:

H0: m1 – m2 = ∆0

Test statistic:

Z0 =

(X − Y ) − ∆ 2 1

0

2 2

s s + n1 n2


Reject H0 if

Ha: m1 – m2 > ∆0

z0 > za

Ha: m1 – m2 < ∆0

z0 < –z a

Ha: m1 – m2 ≠ ∆0

z0 < –z a/2 or z0 > z a/2



Part VI.D.2


The null hypothesis does not always have to be m1 – m2 = ∆0; it could be ≤ or ≥ also. We will use the null hypothesis of H0: m1 – m2 = ∆0. Example 35.13 illustrates the use of hypothesis testing for the differences between two population means. The steps for the hypothesis tests are identical to the six steps outlined earlier.

EXAMPLE 35.13 Two different formulations of gasoline are being tested to study their road octane numbers. Formulation 1 has a variance of octane number of s 12 = 1.45 while the variance for formulation 2 is s 22 = 1.5. Ten samples (n 1 = 10) are selected from formulation 1 and fifteen samples (n2 = 15) are selected from formulation 2. For sample 1, the average octane number was found to be x– = 89 and for sample 2 the average octane number was found to be y– = 91. Is there significant evidence to indicate that a difference exists between the two formulations? Use a 0.05 level of significance.

Solution: The parameter of interest is the difference in average octane number, m 1 – m 2 . Since there is no indication that m 1 > m 2 or m 1 < m 2 , a two-sided test is used. 1. H 0 : m 1 – m 2 = 0

H1: m 1 – m 2 ≠ 0.

2. a = 0.05. 3. Since this is a two-tailed test and a = 0.05, we will reject the null hypothesis if the test statistic is less than –z a/2 or greater than z a/2 , where z a/2 = z0.025 = 1.96. 4. The test statistic is Z0 =

( x − y ) − Δ 0 = ( 89 − 91) − 0 s 12 s 22 + n1 n2

1 . 45 1 . 5 + 10 15

= − 4 . 04 .

5. Since –4.04 < –1.96, we reject the null hypothesis (strong claim). 6. We conclude there is a significant difference in average octane number for the two formulations at the 0.05 level of significance.

Confidence Interval on l 1 – l 2 A 100(1 – a )% confidence interval on the parameter m1 – m2 is given by

(X − Y ) − Z

a /2

s 12 s 22 s 12 s 22 + ≤ m 1 − m 2 ≤ X − Y + Za /2 + . n1 n2 n1 n2

(

)



EXAMPLE 35.14

(X −Y ) − Z

a /2

( 89 − 91) − 1 . 96

s 12 s 22 s 12 s 22 + ≤ m 1 − m 2 ≤ ( X − Y ) + Za / 2 + n1 n2 n1 n2

1 . 45 1 . 5 1 . 45 1 . 5 + ≤ m 1 − m 2 ≤ ( 89 − 91) + 1 . 96 + 10 15 10 15 − 2 . 97 ≤ m 1 − m 2 ≤ − 1 . 03 .

Part VI.D.2

Reconsider the formulation problem given in the previous example. The 95 percent confidence interval on m 1 – m 2 is

We are highly confident that the true difference between the average octane numbers lies between –2.97 and –1.03. Note that the hypothesized value ∆0 = 0 given in the null hypothesis of the previous problem is not contained in this interval. Therefore, we would again say there is a significant difference in the two population means (that is, we would reject the null hypothesis).

Hypothesis Test on l 1 – l 2 , with Population Variances Unknown If the sample sizes are relatively large, regardless of the underlying distributions of the populations of interest, we can use the standard normal distribution as in the case of known variances. The sample variances are used as estimates of the population variances. If the sample sizes are relatively small and the underlying distributions are normally distributed, then the t distribution can be used to conduct hypothesis tests and construct confidence intervals. There are two cases for this situation: 1) the population variances are unknown but assumed roughly equal, and 2) the population variances are unknown and not necessarily equal. The three basic assumptions given earlier still hold for the following methods. Let s 12 represent the variance for sample 1 and s 22 represent the variance for sample 2. Case 1. s 12 = s 22 = s 2 (The population variances are unknown but assumed roughly equal.) Since s 12 and s 22 estimate the same common variance s 2, yet the sample variances may not be equal, we can combine the sample variances to obtain a single point estimate for s 2. This is commonly called the pooled variance: sp2 =

(n1 − 1)s12 + (n2 − 1)s22 n1 + n2 − 2

The pooled standard deviation can be found by taking the square root of the pooled variance. An appropriate test statistic for the hypothesis H0: m1 – m2 = ∆0 is t0 =

(X − Y ) − ∆ sp

1 1 + n1 n2

0

,

which follows a t distribution with k = n1 + n2 – 2.



Part VI.D.2

Hypothesis Test Null hypothesis:

H0: m1 – m2 = ∆0

Test statistic:

t0 =

(x − y ) − ∆ sp

0

1 1 + n1 n2


Reject H0 if

Ha: m1 – m2 > ∆0

t 0 > t a,k

Ha: m1 – m2 < ∆0

t 0 < –t a,k

Ha: m1 – m2 ≠ ∆0

t 0 < –t a/2,k or t 0 > t a/2,k

where k = n1 + n2 – 2 is the total degrees of freedom.

EXAMPLE 35.15 Two vendors of a valve diaphragm present significantly different cost quotations. The wall thickness is the critical quality characteristic. Use the following data to determine whether the average thickness of the products from vendor 1 is greater than that from vendor 2. Assume that the populations are normally distributed and that the samples are independent. Furthermore, the population variances are unknown but assumed to be equal. The test is to be conducted at the 0.05 significance level. The wall thickness measurements for both vendors are: Vendor 1:

86

82

91

88

89

85

88

90

84

87

88

83

Vendor 2:

79

78

82

85

77

86

84

78

80

82

79

76

84

89

Solution: The necessary summary statistics are: Vendor 1: x– = 86.7 s = 2.76 1

Vendor 2: y– = 80.5

s2 = 3.26

n 1 = 14 n 2 = 12

Since the population variances are unknown but assumed equal, the pooled variance and pooled standard deviation should be calculated: sp2 =

(n1 − 1)s12 + (n2 − 1)s22 ( 14 − 1)( 2 . 76 )2 + ( 1 2 − 1)( 3 . 26 )2 = =9 n1 + n2 − 2 14 + 12 − 2

sp = 9 = 3 The parameter of interest is the difference in average wall thickness, m1 – m2 . 1. H 0 : m1 – m2 = 0

Ha: m1 – m2 > 0. Continued



Continued

3. Since this is a right-tailed test and a = 0.05, we will reject the null hypothesis if the test statistic is greater than t a,k where from Appendix P we find t a,k = t 0.05,24 = 1.711. 4. The test statistic is: t0 =

( x − y ) − ∆ 0 = ( 86 . 7 − 80 . 5) − 0 = 5 . 25 . sp

1 1 + n1 n2

Part VI.D.2

2. a = 0.05.

1 1 + 14 12

3

5. Since 5.25 > 1.711, we reject the null hypothesis (strong claim). 6. We conclude that the average thickness of the products from vendor 1 is greater than that from vendor 2 at the 0.05 level of significance.

Confidence Interval on l 1 – l 2 . A 100(1 – a )% two-sided confidence interval on the parameter m1 – m2 is given by

(X − Y ) − t

a /2 , k

(s ) p

(

)

( )

1 1 + ≤ m 1 − m 2 ≤ X − Y + ta /2 , k sp n1 n2

1 1 + . n1 n2

It is important to note that the test statistic based on the t distribution is robust to the common variance assumption. In addition, it is not necessarily a good idea to do formal testing on the equality of two variances (see Box [1954]). Case 2. s 12 ≠ s 22 (The population variances are unknown and not necessarily equal.) The main differences between this case and case 1 are the calculation of the test statistic and the calculation of the degrees of freedom. Since the population variances are not necessarily equal, s 12 and s 22 do not estimate a common population variance; s 12 is an estimate for s 12 , and s 22 is an estimate of s 22, and they can be used directly in the test statistic t0 =

(X − Y ) − ∆ 2 1

2 2

s s + n1 n2

0

.

The test statistic follows a t distribution with degrees of freedom:

n=

(s

2 1

⎛ s12 s22 ⎞ ⎜n +n ⎟ ⎝ 1 2⎠ / n1

) + (s

n1 − 1

2

2 2

2

/ n2

)

2

,

n2 − 1



Part VI.D.2


which is rounded down if it is not an integer value. The same six-step procedure used in case 1 is used for this case. It is important to note that this procedure should be used only if the two variances are very different. A 100(1 – a )% two-sided confidence interval on the parameter m1 – m2 is given by

(X − Y ) − t

a /2 ,n

s12 s12 s2 s2 + ≤ m 1 − m 2 ≤ X − Y + ta /2 ,n 1 + 1 . n1 n2 n1 n2

(

)

Hypothesis Tests and Confidence Intervals for Two Population Proportions p1, p2 There are many situations in which we would like to determine if two populations differ with respect to some proportion of successes or failures. For example, we may wish to determine if two machines from the same process are producing the same proportion of nonconforming items. The hypothesis could be H0: p1 – p2 = 0 where p1 is the proportion of successes from population 1 and p2 is the proportion of successes from population 2. The estimates of p1 and p2 are, respectively, pˆ 1 =

x1 x , pˆ 2 = 2 n1 n2

where n1 is the size of the sample chosen from population 1 n2 is the size of the sample chosen from population 2 x1 is the number of successes out of a sample of size n1 x2 is the number of successes out of a sample of size n2 The parameter of interest is the difference in the two population proportions p1 – p2. The point estimator for this parameter is pˆ 1 – pˆ 2. The expected value and variance for the point estimator are E ( pˆ 1 − pˆ 2 ) = p1 − p2 and V ( pˆ 1 − pˆ 2 ) = s pˆ21 − pˆ 2 =

p1 (1 − p1 ) p2 (1 − p2 ) + . n1 n2

If pˆ 1 and pˆ 2 both follow normal distributions, then the random variable z0 =

( pˆ 1 − pˆ 2 ) − ( p1 − p2 ) p1 (1 − p1 ) p2 (1 − p2 ) + n1 n2



follows a standard normal distribution. Under the assumption that the null hypothesis (H0: p1 – p2 = 0) is true, then the random variable Z can be written as

Part VI.D.2

z0 =

( pˆ 1 − pˆ 2 ) − 0 ⎛ 1 1⎞ pˆ (1 − pˆ ) ⎜ + ⎟ ⎝ n1 n2 ⎠

where pˆ is the proportion resulting from the combination of the two samples (estimate of the overall proportion when we are testing p1 = p2). The formula is pˆ =

x1 + x 2 . n1 + n2

Hypothesis Test Null hypothesis:

H0: p1 – p2 = 0

Test statistic:

z0 =

( pˆ 1 − pˆ 2 ) − 0 ⎛ 1 1⎞ pˆ (1 − pˆ ) ⎜ + ⎟ ⎝ n1 n2 ⎠

The alternative hypotheses and rejection regions are Alternative hypothesis

Reject H0 if

Ha: p 1 – p 2 > 0

z0 > za

Ha : p 1 – p 2 < 0

z0 < –z a

Ha : p 1 – p 2 ≠ 0

z0 < –z a/2 or z0 > z a/2

EXAMPLE 35.16 Two machines produce the same parts. A random sample of 1500 parts from machine 1 has 36 that are nonconforming, and a random sample of 1680 parts from machine 2 has 39 that are nonconforming. Is there evidence to suggest that machine 1 has a higher nonconforming rate than machine 2? Test at the 0.01 level of significance.

Solution: For this problem: • p 1 represents the proportion of nonconforming units produced by machine 1. • p 2 represents the proportion of nonconforming units produced by machine 2. • pˆ1 is the sample proportion estimating p 1; it is pˆ 1 =

36 x1 = = 0 . 024 . n1 1500 Continued



Part VI.D.2

Continued

• pˆ2 is the sample proportion estimating p 2; it is found to be pˆ 2 =

39 x2 = = 0 . 0232 . n2 1680

• pˆ is the proportion resulting from the combination of the two samples (estimate of the overall proportion when we are testing p 1 – p 2 = 0). It is found to be pˆ =

36 + 39 75 x1 + x2 = = = 0 . 0236 . n1 + n2 1500 + 1680 3180

The parameter of interest is the difference in the two population proportions p 1 – p 2 . 1. The null and alternative hypotheses are H0: p1 – p2 = 0

Ha: p 1 – p 2 > 0.

2. The level of significance is 0.01. 3. Since this is a right-tailed test, we will reject the null hypothesis if the test statistic is greater than z a = z0.01 = 2.33. 4. The test statistic is z0 =

( pˆ 1 − pˆ 2 ) ⎛ 1 1⎞ pˆ ( 1 − pˆ ) ⎜ + ⎟ ⎝ n1 n2 ⎠

=

( 0 . 024 − 0 .00 232) 1 ⎞ ⎛ 1 + 0 . 0236( 1 − 0 . 0236 ) ⎜ ⎝ 1500 1680 ⎟⎠

= 0 . 1 4 8.

5. Since 0.148 < 2.33, we can not reject the null hypothesis (weak claim). 6. There is insufficient evidence to conclude that machine 1 has a higher nonconforming rate than machine 2 at the 0.01 level of significance.

A 100(1 – a )% confidence interval on the parameter p1 – p2 is ⎛ 1 1⎞ ⎛ 1 1⎞ ( pˆ 1 − pˆ 2 ) − za /2 pˆ (1 − pˆ ) ⎜ + ⎟ ≤ p1 − p2 ≤ ( pˆ 1 − pˆ 2 ) + za /2 pˆ (1 − pˆ ) ⎜ + ⎟ . ⎝ n1 n2 ⎠ ⎝ n1 n2 ⎠

Hypothesis Tests and Confidence Intervals for Two Population Variances r 12, r 22 A test on the variances of two populations can be used to determine if the variance of one is greater than the other. For example, the null hypothesis could be H0: s 12 = s 22 where s 12 is the variance of population 1 and s 22 is the variance of population 2. This test uses the F distribution presented in Chapter 34. Let Y1, Y2, . . . , Yn1 be a random sample from a normal distribution with variance s 12 and W1, W2, . . . , Wn2 be a random sample from a normal distribution with variance s 22. Furthermore, assume that the samples are independent of each other and s12 and s22 are the sample variances for Yi ’s and Wj ’s, respectively. The random variable



F=

follows an F distribution with k1 = n1 – 1 and k2 = n2 – 1 degrees of freedom. Hypothesis Test Null hypothesis:

H0: s 12 = s 22

Test statistic:

F0 = s12/s22

Part VI.D.2

s12 / s 12 s22 / s 22

The alternative hypotheses and rejection regions are Alternative hypothesis

Reject H0 if

Ha: s > s

F 0 > Fa,k1,k2

2 1

2 2

Ha: s 12 < s 22

F 0 < F 1– a,k1,k2

Ha : s ≠ s

F 0 < F 1– a/2,k1,k2 or F 0 > Fa/2,k1,k2

2 1

2 2

The critical values for the F distribution can be found in Appendices G, H, and I. The notation Fa,k1,k2 is the F value with area of a to its right, k1 represents the numerator degrees of freedom, and k2 represents the denominator degrees of freedom. A probability from this table represents the area under the curve and to the right of the F value of interest. For example, for a 0.05 level of significance with k1 = 15 and k2 = 5 degrees of freedom, the F value would be F0.05, 15, 5 = 4.62. The F table provides values for specific values of a (level of significance). To find critical values when the area to the right is 1 – a, such as F1–a,k1,k2, we have to use the following relationship: F1−a , k1 , k2 =

1 Fa , k2 , k1

To illustrate, suppose we want to find a critical value for a left-tailed test where k1 = 10, k2 = 8, and a = 0.05. Then F1–a,k1,k2 = F0.95,10,8 and F0 . 95 ,10 ,8 =

1 1 = = 0 . 326 . F0 . 05 ,8 ,10 3 . 07

EXAMPLE 35.17 Two chemical companies can supply a particular material. The concentration of an element in this material is important. The mean concentration for both suppliers is approximately the same, but we suspect that the variability in concentration may differ between the two companies. The standard deviation of concentration in a random sample of n 1 = 10 batches produced by company 1 is s1 = 3.8 g/l, while for company 2, a random sample of n 2 = 16 batches yields s2 = 4.2 g/l. Using a level of significance of 0.10, we would like to determine if there is sufficient evidence to conclude that the two population variances differ. Continued



Part VI.D.2

Continued

Solution: Let: • s 12 be the population variance of the element concentration in the material from company 1 • s 22 be the population variance of the element concentration in the material from company 2 1. The null and alternative hypotheses are H 0 : s 12 = s 22

H1: s 12 ≠ s 22.

2. The level of significance is a = 0.10. 3. There are two rejection regions since we have a two-tailed test. Furthermore, the degrees of freedom are k1 = n 1 – 1 = 9 and k 2 = n 2 – 1 = 15. The critical values are Fa/2,k1,k 2 = F 0.05,9,15 = 2.59 and F0 . 950 ,9 ,15 =

1 1 1 = = = 0 . 332 . Fa / 2 ,k2 ,k1 F0 . 05 ,15 ,9 3 . 01

Therefore, reject the null hypothesis if the test statistic is less than 0.332 or greater than 2.59. 4. The test statistic is s12 ( 3 . 8 ) = 0 . 819 . = s22 ( 4 . 2 )2 2

F0 =

5. Since 0.332 < 0.819 < 2.59, we can not reject the null hypothesis at the 0.10 level of significance (weak claim). 6. There is insufficient evidence to conclude that the variances of the element concentration in this material from the two suppliers are not equal.

A 100(1 – a )% two-sided confidence interval on the parameter s 12/s 22 is given by s12 s 12 s12 ≤ ≤ F . F s22 1−a /2 , k1 , k2 s 22 s22 a /2 , k1 , k2 The confidence interval on the ratio of the two variances can be used to determine if there is a significant difference between the two variances. In hypothesis testing we assume the null hypothesis is true, that is, H0: s 12 = s 22. We can rewrite this equality as s 12 = 1. s 22



0 . 217 ≤

s 12 ≤ 2 . 555 . s 22

Part VI.D.2

When constructing a two-sided confidence interval on the ratio of the two variances, we would reject the null hypothesis if the value 1 is not contained within that interval. For example, the 95 percent two-sided confidence interval on the ratio of variances of the previous example is

Therefore, we are highly confident that the true ratio of the variances lies between 0.217 and 2.555. Since this interval contains the value 1, we would conclude based on our samples that there is no statistically significant difference between the two variances.

The p-value Approach to Hypothesis Testing In this chapter, hypothesis testing has been presented using the critical value (or fixed significance level) approach and rejection regions. Critical values are determined based on a stated level of significance (a ), among other quantities. The critical value approach is somewhat lacking for two reasons: 1) it does not completely quantify the degree to which a null hypothesis is rejected or not rejected, and 2) it imposes a specific significance level on the practitioner or others making the decision. For example, suppose a right-tailed test on a population mean with a = 0.05 was carried out. It was reported that the critical value found from the t table was t = 2.306, and the null hypothesis was rejected—but you are not told the value of the test statistic itself. If you are given no further information, can you determine what the test statistic value might have been? Obviously the value of the test statistic was greater than 2.306 (since the null hypothesis was rejected), but by how much? There is no indication of whether the test statistic was 2.310 or 10.310 given just this information. In addition, the decision was based on a specific level of significance. If a 0.05 level of significance is used, but after conducting the test it is determined that a 0.01 level of significance should have been used, new critical values must be determined. In many engineering problems, an acceptable level of significance may be known, but not in every situation. A second approach that offers some measure of the degree to which the test statistic is significant involves the use of p-values. A p-value is the smallest level of significance at which the null hypothesis would be rejected. It can be thought of as how likely it is that we should have obtained that value of the test statistic or more extreme (more extreme than the value of the test statistic we did get if the null hypothesis is really true). A small p-value is evidence against the null hypothesis in favor of the alternative. For example, suppose we are testing H0: m = 50 against Ha: m > 50 and the test statistic is found to be z0 = 2.47. The p-value for this test would be P(Z > 2.47) = 0.0068. That is, the probability that we should have obtained a test statistic of 2.47 or more extreme (in this case larger than 2.47, since we have a right-tailed test) if H0: m = 50 is true, is 0.0068. This is a highly unlikely event. It is highly unlikely that we should have obtained a test statistic of 2.47 if the mean really is 50. But we did



Part VI.D.2


get a test statistic of 2.47, so what went wrong? Remember that the claim m = 50 is a hypothesis that can be proven incorrect (based on collected data). Therefore, the null hypothesis is probably false. Decision. If a level of significance is predetermined (but it does not have to be) then the decisions would be as follows: • If p-value < a, reject H0. • If p-value > a, do not reject H0. Using the p-value approach allows the practitioner flexibility in making decisions. If the significance level is changed for a particular test, no new calculations need to be done in order to make a decision. The p-value will not change for a test even if the level of significance does. The p-value is easy to interpret, and most computer software packages will report a p-value for a hypothesis test. Reconsider the wall thickness example for two competing vendors given in Example 35.15. The data and some of the results are given here: Vendor 1:

86

82

91

88

89

85

88

90

84

87

88

83

Vendor 2:

79

78

82

85

77

86

84

78

80

82

79

76

84

89

The parameter of interest is the difference in average wall thickness, m1 – m2 and the hypotheses are H0: m1 – m2 = 0 and H1: m1 – m2 > 0. The level of significance was a = 0.05, and we concluded that since our test statistic (5.25) was greater than the critical value (1.711), we would reject the null hypothesis. The problem was worked again, this time using a statistical software package, with the following output:

Two-sample T for Vendor1 versus Vendor2 Difference = mu (Vendor1) – mu (Vendor2) T-Test of difference = 0 (vs >): T-Value = 5.27 P-Value = 0.000 DF = 24

The p-value is given in bold and reported to be 0.000 (this value is most likely some extremely small number very close to zero; so for all practical purposes, the p-value is zero). Since the p-value < a (that is, 0.000 < 0.05), we would reject the null hypothesis. What is telling about the p-value is that we could easily change the level of significance and be able to make a decision without having to do any further calculations. The p-value will remain 0.000 regardless of the level of significance. The six-step hypothesis testing procedure given in this chapter would also apply when using p-values. The steps would be: 1. State H0 and Ha.



2. State a (optional).

4. Find the p-value. 5. Reject or do not reject H0; if a is given, then reject H0 if p-value < a, otherwise do not reject H0. 6. State your conclusions in terms of the problem statement.

Part VI.D.2

3. Calculate the test statistic.

The smaller the p-value, the stronger the evidence against the null hypothesis and in favor of the alternative hypothesis. It is important to note that the p-value approach and the critical value approach will lead to the same conclusion for the same problem. This is true as long as all quantities and assumptions are identical when using both approaches. Finally, with all of the procedures given in this chapter, it is recommended that the calculations be done using a reliable statistical software package.

Statistical versus Practical Significance In some situations, it may be possible to detect a statistically significant difference between two populations when there is no practical difference. In hypothesis testing, the goal is to make a decision about a claim or hypothesis. The decision as to whether or not the null hypothesis is rejected in favor of the alternative hypothesis is based on a sample taken from the population of interest. If the null hypothesis is rejected, we say there is statistically significant evidence against the null hypothesis in favor of the alternative hypothesis. But statistical significance does not imply practical significance. Rejecting the null hypothesis in favor of the alternative hypothesis by a very small margin may be the result of a relatively large sample size. Large sample sizes will almost always lead to rejection of the null hypothesis. Illustration. Consider an automobile manufacturer’s claim that one particular make of car averages 31 miles per gallon on the highway. A consumer group tests 75 cars of the same make under identical conditions and finds the average to be 30.6 miles per gallon. We could conduct a hypothesis test of H0: m = 31 versus Ha: m < 31. The sample average is x– = 30.6. If we are able to reject H0 in favor of Ha, we say there is statistically significant evidence to indicate that the true average is less than 31 mpg. But, is 30.6 really different from 31 in the practical sense? In this situation, a statistical significance was found, but not necessarily a practical significance in the difference between the hypothesized value (31 mpg) and the estimated value (30.6 mpg). Rejecting the null hypothesis in favor of the alternative hypothesis by a very small margin may be the result of a relatively large sample size. Again, larger sample sizes can often result in statistically significant results even though the difference may not be of practical significance. In summary, a rejected null hypothesis implies statistical significance but not necessarily practical significance. Some practitioners prefer using confidence intervals for making decisions since they allow one to see if a practical difference exists.



Part VI.D.2

Significance Level, Power, Type I and Type II Errors Since every hypothesis test uses samples to infer properties of a population based on analysis of a sample, there is some chance that although the analysis is flawless the conclusion may be incorrect. These sampling errors are not errors in the usual sense because they can not be corrected (without using 100 percent sampling with no measurement errors). The two possible types of errors that can occur in hypothesis testing are the type I error and type II error briefly introduced earlier in this chapter. A type I error occurs when a true null hypothesis is rejected. The probability of committing a type I error is denoted a —the significance level in hypothesis testing. The critical values for a hypothesis testing procedure are based on a predetermined level of significance. That is, the maximum allowable probability of rejecting a true null hypothesis (a ) is fixed for a particular problem. A type II error occurs when a false null hypothesis is not rejected. The probability of committing a type II error is denoted b. A summary of the possible decisions and errors are given in the following table: Result of Hypothesis Test Reject H 0 H0 is actually

Fail to reject H 0

True

Type I error

No error

False

No error

Type II error

Again, a = P(type I error) = P(rejecting H0 when in fact H0 is true) b = P(type II error) = P(failing to reject H0 when in fact H0 is false). In general, it is desirable to have small a and b, but there is often a trade-off. For a fixed sample size in hypothesis testing, decreasing a will result in an increase in b. Because of the manner in which the null and alternative hypotheses are specified in hypothesis testing, it is generally true that committing a type I error is more serious than committing a type II error. Therefore, controlling the probability of committing a type I error (setting a ) is often a higher priority than controlling the probability of committing a type II error. In fact, it is difficult to specify an exact value of b since that would require knowing the true value of the parameter being tested. The power of a hypothesis test is defined as the probability of correctly rejecting a false null hypothesis. The power is given by 1 – b, since b is the probability of failing to reject the null hypothesis when it is false. The power provides some measure of the test’s ability to detect differences. This ability to detect differences is often referred to as the sensitivity of the hypothesis test.



3. PAIRED-COMPARISON TESTS Part VI.D.3

Define and use paired-comparison (parametric) hypothesis tests, and interpret the results. (Apply) Body of Knowledge VI.D.3

Paired-comparison hypothesis testing involves a two-sample t-test for two samples that are believed to be dependent, that is, when an observation from one sample can be logically paired with an observation from the other sample. The pairing of two observations is based on some characteristic they have in common. By pairing the data when necessary, we can reduce the effect of the common characteristic that may influence the results. To illustrate, 40 people are placed on the same diet program. Each person is weighed on day 1 (before weight), put through the program, then weighed again at the end of the program (after weight). The difference in the person’s before weight and after weight is recorded. This is a paired comparison—pairing each individual person’s before weight with their after weight (it would not make sense to compare the before weight of person X with the after weight of person Y, due to individual differences in people).

When Should Pairing Be Used? The data from two samples should be paired when there is a logical relationship between the two observations. The following set of data represents heart rates (in beats per minute) for individuals who used two types of exercise equipment, A and B. A

161

172

166

189

180

B

155

191

187

174

171

There is no indication of whether the heart rates recorded involved five people using both types of equipment or ten people—five using type A and five using type B. There is no indication that the data should be paired from equipment A to equipment B. Consider the next set of data, again representing the heart rates of people who used the exercise equipment: Person

1

2

3

4

5

A

161

172

166

189

180

B

155

191

187

174

171



Part VI.D.3


Now we have more information that indicates the heart rates have a characteristic in common. Since basal heart rates vary a great deal from person to person, we would pair the heart rates by person in order to minimize the effect of individual differences. Therefore, if a significant difference is found, there is a better chance that the difference is due to the type of equipment and not the person using it. If there is any indication that the data should be paired in a particular problem, then pairing should be done (that is, if in doubt, pair the data).

Procedure for Paired Comparisons Suppose we have n independently selected pairs given by (X1, Y1), (X2, Y2), . . . , (Xn, Yn). Furthermore, let E(Xi) = m1 and E(Yi) = m2. The paired-comparison test is a test conducted on the differences between the two groups. Define the differences as Di = Xi – Yi for all n pairs. Assume the differences Di are normally distributed with the following parameters: • Mean difference mD, where mD = m1 – m2 • Variance of the differences s 2D For a sample of n independently selected pairs (Xi, Yi), let di (i = 1, 2, . . . , n) repre– sent the actual differences from the sample. The sample mean d and sample standard deviation sd for the differences are n

d=

∑d

i

i=1

n

and n

sd =

∑(d − d ) i

i=1

n−1

.

It is important to note that once the differences are calculated, the paired t-test is equivalent to the t-test for a single population mean presented earlier. The test statistic that will be used for the paired t-test is t0 =

d sd / n

,

which follows a t distribution with k = n – 1 degrees of freedom. The six-step procedure for conducting a paired t-test is: 1. State the null and alternative hypotheses for the problem. We will restrict ourselves to the case of no difference between the two populations. That is, H0: m D = 0, and the alternative hypothesis can be one of the three possible alternatives given earlier (Ha: m D < 0, m D > 0, or m D ≠ 0). 2. State the level of significance. 3. Find the rejection region.



4. Calculate the test statistic, t0.

6. State the conclusions in terms of the problem statement.

Part VI.D.3

5. Compare the test statistic t0 to the critical value found in step 3. If the test statistic falls into the rejection region, then reject the null hypothesis (strong claim). If the test statistic does not fall into the rejection region, then state that there is insufficient evidence to reject the null hypothesis (weak claim).

EXAMPLE 35.18 Consider the study on two types of exercise equipment given earlier. The following set of data represents heart rates (in beats per minute) for individuals who used the two types of exercise equipment, A and B. The last row represents the differences in heart rate for each person, di = A i – Bi for i = 1, 2, . . . , 5. Person

1

2

3

4

5

A

161

172

166

189

180

B

155

191

187

174

171

di = A i – B i

6

–19

–21

15

9

Is there a significant difference in heart rate due to the type of exercise equipment used? Use a 0.05 level of significance. We will assume that the differences follow a normal distribution.

Solution: • Let m1 represent the mean heart rate after using equipment A. • Let m2 represent the mean heart rate after using equipment B. • The logical pairing involves the differences in heart rates for both types of equipment by person. There are n = 5 heart rates in each sample. • Let mD represent the true mean difference between the two populations m D = m1 – m 2 . The necessary summary statistics are n

d= and n

sd =

∑ (d i=1

i

−d

)

i=1

n

i

=

6 + ( − 19) + ( − 21) + 15 + 9 = −2.0 5

2

n−1

(6 − ( − 2 . 0 )) + ( − 19 − ( − 2 . 0 )) + ( − 21 − ( − 2 . 0 )) + ( 15 − ( − 2 . 0 )) + ( 9 − ( − 2 . 0 )) 2

=

∑d

2

2

5− 1

2

2

= 16 . 76 .

Continued



Part VI.D.4

Continued

The steps are: 1. H 0 : mD = 0

Ha: mD ≠ 0.

2. a = 0.05. 3. Since this is a two-tailed test, reject H 0 if the test statistic is less than –t a/2,k or greater than t a/2,k where t a/2,k = t 0.025,4 = 2.776. 4. Calculate the test statistic: t0 =

d −2.0 = = − 0 . 27 . sD / n 16 . 76 / 5

5. Since –2.776 < –0.27 < 2.776, we can not reject the null hypothesis (weak claim). 6. There is insufficient evidence to conclude that the type of exercise equipment significantly affects heart rate at the 0.05 level of significance.

A 100(1 – a )% two-sided confidence interval on the parameter m D is given by ⎛ s ⎞ ⎛ s ⎞ d − ta /2 , k ⎜ d ⎟ ≤ m ≤ d + ta /2 , k ⎜ d ⎟ . ⎝ n⎠ ⎝ n⎠

4. GOODNESS-OF-FIT TESTS

Define and use chi square and other goodness-of-fit tests, and interpret the results. (Apply) Body of Knowledge VI.D.4

Chi-square and other goodness-of-fit tests help determine whether a discrete sample has been drawn from a known population. The probability distribution may be of a specific form, such as the Poisson, binomial, geometric, and so on, or it may be simply a table of outcomes and their assumed probabilities. For example, suppose that all rejected products have exactly one of four types of nonconformities (that render them nonconforming) and historically they have been distributed as follows:



Percent of nonconforming products

Nonconformity

16%

Paint blister

28%

Decal crooked

42%

Door cracked

14%

Total

Part VI.D.4

Paint run

100%

Data on rejected parts for a randomly selected week in the current year are: Number of nonconforming products

Nonconformity Paint run

27

Paint blister

60

Decal crooked

100

Door cracked

21

Total

208

The question we need to answer: Is the distribution of nonconformity types different from the historical distribution? The test that answers this question is referred to as the c 2 goodness-of-fit test. To get a feel for this test, construct a table that displays the number of nonconforming units that would be expected in each category if the sample exactly followed the historical percentages (the historical percentages are used as estimates of the probabilities that the nonconformities are present on the product). The expected number will be referred to as the expected frequency. The expected frequency is found by multiplying the total number of items in the sample n by the probability for a particular category.

Nonconformity

Observed frequency (Oi)

Probability (pi)

Expected frequency (Ei) [Ei = npi]

Paint run

27

0.16

33.28

Paint blister

60

0.28

58.24

Decal crooked

100

0.42

87.36

Door cracked

21

0.14

29.12

Total

n = 208

1

The question to be decided is whether the difference between the expected frequencies and observed frequencies is sufficiently large. If the difference is large, there may be significant evidence to conclude that the distribution the current sample came from is not the same as the historical distribution; we may even have enough evidence to conclude that the historical (assumed) distribution is no longer valid.



Part VI.D.4

The test statistic that can be used to determine if the assumed distribution is still valid is k

c =∑ 2 0

(O − E ) i

2

i

.

Ei

i=1

It can be shown that the test statistic follows a chi-square distribution with k – 1 degrees of freedom (where k = number of categories). Let pi represent the proportion of the population that falls into the ith category, for i = 1, 2, . . . , k. Let pi,0 represent the hypothesized value of pi. The null and alternative hypotheses would be: H0: p1 = p1,0; p2 = p2,0; . . . , pk = pk,0 Ha: pi ≠ pi,0 for at least one i = 1, 2, . . . , k The procedure for conducting a goodness-of-fit test is as follows: 1. H0: p1 = p1,0; p2 = p2,0; . . . , pk = pk,0 Ha: pi ≠ pi,0 for at least one i = 1, 2, . . . , k. 2. State the level of significance a. 3. Determine the rejection region. For this test the critical value 2 is c a,k–1 . 4. Calculate the test statistic: k

c =∑ 2 0

(O − E )

i=1

i

i

Ei

2

.

5. If c 20 ≥ c 2a,k–1, reject H0; otherwise do not reject H0. 6. State the conclusions in terms of the problem statement.

EXAMPLE 35.19 We will complete the goodness-of-fit test for the problem involving nonconforming products using a five percent level of significance. 1. H 0 : p 1 = 0.16; p 2 = 0.28; p 3 = 0.42; p 4 = 0.14 Ha: pi ≠ pi,0 for at least one i = 1, 2, . . . , 4 2. a = 0.05. 2 2 = c 0.05,3 = 7.815. 3. For this test the critical value is c a,k–1

4. Calculate the test statistic: k

c 02 = ∑

(Oi − Ei )2 = ( 27 − 33 . 28 )2 + (60 − 58 .22 4 )2 + ( 100 − 87 . 36 )2 + ( 21 − 29 . 12)2

i=1

Ei

33 . 28

58 . 24

87 . 36

29 . 12

= 5 . 33 Continued



Continued

6. We conclude that the data is consistent with the assumed historical probabilities at the 0.05 level of significance.

Part VI.D.5

5. Since 5.33 < 7.815, we can not reject H 0 (weak claim).

The chi-square goodness-of-fit test is valid as long as the expected frequencies are not too small. Some recommendations for a minimum value have included 3, 4, and 5. Other recommendations have been that some of the expected frequencies can be as small as 1 or 2 as long as most of expected frequencies exceed 5. For more details on other applications of the chi-square test and further recommendations see Devore (2007), Montgomery and Runger (2006), and Vining and Kowalski (2006).

5. ANALYSIS OF VARIANCE (ANOVA)

Define and use ANOVAs and interpret the results. (Analyze) Body of Knowledge VI.D.5

One-Way ANOVA If we are interested in comparing more than two samples, the previous tests are not valid. Suppose we want to compare a populations. Sometimes the populations are referred to as treatments or levels of a factor. Let m1, m2, . . . , m a represent the means for the populations. The goal is to determine if the treatments applied significantly affect the outcome or response of interest.

EXAMPLE 35.20 Alternative energy sources to traditional fossil fuels are in high demand. Several variables are believed to influence the conversion of waste vegetable oil into biodiesel fuel. One variable of interest is the amount of catalyst (%) at three levels—0.6, 1.0, and 1.4—used in the conversion process. The response of interest is conversion rate (wt%) (larger values indicate that more of the waste vegetable oil was successfully converted into useable biodiesel fuel). The experiments are conducted in random order and with the following results: Continued



Continued

Part VI.D.5

Catalyst (%) 0.6

1.0

1.4

71.34 76.11 73.16 76.02

84.62 78.21 82.39 76.55

78.33 76.89 71.42 76.60

In this study: • Some questions of interest are: Does the amount of catalyst have a significant effect on the conversion rate? If so, which catalyst amount will result in a high conversion rate? • Catalyst is the factor of interest or independent variable. • The factor of interest (catalyst) has three levels (0.6%, 1.0%, and 1.4%). You will also see levels referred to as treatments or groups. In this example, we would say there are three treatments applied or three groups being studied. • Conversion rate is the response of interest (or dependent variable). • There are four replicates (n = 4) for each level of catalyst.

Assumptions. Three important assumptions for the use of a one-way analysis of variance are: 1. The observations follow a normal distribution (that is, the populations are normally distributed). 2. The observations are independent. 3. The treatments have constant variance (homogeneity of variances). In summary, the treatment distributions (populations) for all a treatments should be normally distributed each with the same variance s 2. An additional assumption that is not always included deals with the number of replicates. It is not necessary that each level of the factor or treatment have the same number of replicates. But large differences in the number of observations from group to group can affect the validity of the analysis. We assume that the number of replicates n will be equal or near equal for all treatments. For more details see Devore (2007), Montgomery and Runger (2006), and Vining and Kowalski (2006). It should be noted that the analysis of variance procedure outlined next is fairly robust to slight departures from these assumptions. The assumptions should always be verified. This is discussed later in this section.

General Notation and Methods Suppose we have one factor of interest with a levels of that factor (we could also say there are a treatments being compared.) Let Yij represent the jth response in the ith treatment where i = 1, 2, . . . , a and j = 1, 2, . . . , n. For example, the third



Totals Averages

1

Treatment 2

y 11 y 12

y 21 y 22

y 1n y1. y–

1.

...

a ya1 ya2

y 2n

... ... ... ...

yan

y2. y–

ya. – y

y.. – y ..

2.

a.

Part VI.D.5

observation for the second treatment (catalyst amount = 1.0%) given earlier would be denoted y23 = 82.39. There are a total of a × n observations in the experiment. A general table of results could be set up as:

where yi. = the sum of the n observations in the ith treatment (the dot subscript indicates summation over the subscript it replaces) y.. = the sum of all a × n observations y– = average of the n observations in the ith treatment i.

y–.. = average of all a × n observations The hypotheses of interest are H0: m1 = m2 = . . . = m a H1: at least two of the means differ (that is, mi ≠ mj) The quantities needed to determine if a significant difference exists among the treatments (or levels of the factor) are the sum of squares and the degrees of freedom.

Sums of Squares and Degrees of Freedom The total sum of squares, denoted SST, is an important quantity that provides a measure of the total overall variability in the response: a

n

(

SST = ∑ ∑ yij − y.. i=1 j=1

)

2

where yij is a single observation and y–.. is the average of all a × n responses. The total sum of squares can be partitioned into two sources of variability: variability due to the treatments applied (SSTreatments) and variability due to error or unknown sources (SSE). In other words: SST = SSTreatments + SSE The sum of squares due to treatments (SSTreatments) is a portion of the total sum of squares (SST). It is a quantity that measures the proportion of total variability that can be explained by or that is due to the different treatments applied:



a

SS Treatments = n∑ ( yi. − y.. )

2

Part VI.D.5

i=1

The error sum of squares (SSE) is that portion of the total sum of squares (SST) that represents the inherent variability. This is variability that is not due to the treatments applied or levels used. In conducting an experiment involving a single factor we assume that all variables (other than the treatments) that could possibly influence the response are held constant; any variability that can not be attributed to the treatment is said to be error. Under these conditions the “leftover” variability is considered inherent. For example, if temperature is known to possibly influence conversion rate in our previous example, but temperature is not of interest at this point, we would hold temperature (this is the temperature of a water bath in the reaction experiment) at a constant and only vary the factor that is of interest (such as catalyst). The error sum of squares can be found by subtraction: SSE = SST − SSTreatments It is important to note that if we are trying to show that the treatments cause a significant effect on the response, then we want the variability due to treatments to be large as compared to the inherent variability. It is desirable to have the variability due to error or unknown sources to be as small as possible (that is, we want SSE to be small). A ratio involving the SSTreatments and SSE is used to reject or not reject the hypothesis of interest that all treatment means are equal: H0: m1 = m2 = . . . = m a This ratio involving the SSTreatments and SSE, (whose exact calculations will be shown next) should be significantly large in order to reject the null hypothesis. A large value of the ratio indicates that most of the total variability is attributed to the treatments applied and is not just variability due to error. If the error variability was comparable to (or larger than) the variability due to treatments, this would indicate that there is very little difference in treatments applied and that most of the variability is uncontrollable. Degrees of freedom are associated with each source of variability. The degrees of freedom are necessary values in the computation of a test statistic and are as follows: • The total degrees of freedom are an – 1 • For a treatments, the degrees of freedom are a − 1. • The degrees of freedom for error are a(n – 1). • Notice that the total degrees of freedom can be partitioned into degrees of freedom for treatments and degrees of freedom for error: an – 1 = a − 1 + a(n − 1) The degrees of freedom are used in the ratio involving the sums of squares discussed previously. The sum of squares divided by the appropriate degrees of freedom provides a measure of variability adjusted for sample size and number of treatments in the study. These resulting measures are referred to as mean squares



(MS). Under H0, they are estimates of the error variance. The mean square for treatments is SS Treatments . a−1

The error mean square is MS E =

SS E . a(n − 1)

Part VI.D.5

MS Treatments =

Now, we can directly compare the two mean squares. If MSTreatments > MSE, then there may be evidence that the treatments have a significant affect on the response. The ratio of the mean squares can be used as a test statistic to make a decision about rejecting or not rejecting the null hypothesis H0. The ratio is our test statistic, denoted by F0: F0 =

MS Treatments MS E

which can be shown to follow an F distribution with k1 = a – 1, and k2 = a(n – 1) degrees of freedom. If F0 is large, then we may have evidence that the different treatments significantly affect the response of interest. Recall the section on testing two population variances. The F distribution was appropriate for modeling the ratio of two variances. Therefore we will compare the test statistic F0 to an appropriate critical value found from the F distribution (Appendices G, H, and I). The appropriate critical value is given by Fa,a–1,a(n–1) where a is the probability of a type I error, discussed in previous sections, a − 1 is the degrees of freedom for the numerator of F0, and a(n − 1) is the degrees of freedom for the denominator of F0. Therefore, if F0 > Fa,a–1,a(n–1), we will reject the null hypothesis and conclude that the treatments or levels are significantly different at the a level of significance. We only reject the null hypothesis for large F0 since a large ratio indicates that the variability due to the treatments is larger than the variability due to error (that is, MSTreatments >> MSE).

p-values The p-values discussed previously can also be calculated for the F test given here. The same interpretation would apply: if the p-value < a, then reject the null hypothesis, otherwise do not reject the null hypothesis. P-values are easy to interpret and provided by most statistical software packages when conducting an analysis of variance. The degrees of freedom (df), sum of squares (SS), mean squares (MS), F value, and p-value are often summarized in an analysis of variance (ANOVA) table. The table for a one-way ANOVA is shown as Table 35.2. The calculations and the resulting ANOVA table can be easily obtained using modern computer software or the formulas given in this section. Software packages that have statistical capabilities will automatically report some form of this ANOVA table.



Part VI.D.5

Table 35.2

One-way ANOVA table.

Source of variability

df

SS

MS

F

p-value

a–1

SSTreatments

MSTreatments

F0

P(F > F0)

Error

a(n – 1)

SSE

MSE

Total

an – 1

SST

Treatments

We will now return to our conversion rate of biodiesel fuel example presented at the beginning of this section. In this example, we will only set up the null and alternative hypotheses and present the results of an ANOVA table obtained using a commercially available and reliable statistical package.

EXAMPLE 35.21 The data are reproduced here with the factor of interest (treatments) being amount of catalyst (%) and the response conversion rate. Is there significant evidence to conclude that amount of catalyst significantly affects the conversion rate? Catalyst (%) 0.6

1.0

1.4

71.34 76.11 73.16 76.02

84.62 78.21 82.39 76.55

78.33 76.89 71.42 76.60

Solution: The null and alternative hypotheses are H 0 : m1 = m 2 = m 3 Ha: at least two mi are different, for i ≠ j. Before a formal analysis is conducted on the data, it is often informative to display the results graphically. The box plot is one such appropriate graphical display. Box plots for catalyst amount are shown in Figure 35.1. Based on the box plots, it appears that the catalyst amount of 1.0 percent results in higher conversion rates than either 0.6 percent or 1.4 percent. There does not appear to be a difference in conversion rate between catalyst amounts of 0.6 percent and 1.4 percent. Since interpretation of the graphical displays can be subjective, a more formal analysis such as an analysis of variance would be more reliable. The analysis of variance table is: MS

F

p-value

Catalyst


df 2

84.92

SS

42.46

4.50

0.044

Error

9

85.01

9.45

Total

11

169.93

Continued



Continued

Part VI.D.5

86

Conversion rate (%)

84 82 80 78 76 74 72 70 0.6

Figure 35.1

1.0 Amount of catalyst (%)

1.4

Box plots for catalyst amount.

The p-value is reported as 0.044. Since the p-value is small, we can reject the null hypothesis and conclude that there is evidence that amount of catalyst affects the conversion rate of waste vegetable oil into biodiesel fuel.

At this point, the assumptions given earlier should be verified. Some simple tools can be used to verify that the normality and constant variance assumptions are valid. To assess normality, a normal probability plot of the observations can be constructed. A simple method for assessing constant variance is to examine the standard deviations of each treatment. A quick and dirty rule of thumb is that the constant variance assumption is plausible as long as the largest treatment standard deviation is not much more than two times the smallest treatment standard deviation. (See Devore [2007] for more details on these and other methods.) More formal methods for assessing constant variance involve analyzing residuals. These methods will be discussed in later chapters. In order to adequately assess independence we must have the order in which the data were collected. Without the order, it is difficult to determine the validity of the independence assumption. In addition, if the experiment was conducted randomly (carried out randomly) it is often assumed that this randomization will minimize any dependency among the observations.

Two-Way ANOVA The two-way ANOVA hypothesis test can be used when there are two factors of interest in the experiment. In our biodiesel fuel example there was one factor (catalyst amount) with three levels. When more than one factor is under investigation in an experiment, a factorial experiment should be used. A factorial experiment is one where all possible combinations of the factor levels are investigated.



A 1 2

32

25.0 Mean response

Mean response

30

Part VI.D.5

A 1 2

27.5

28 26

22.5 20.0 17.5

24 15.0 22 1

2 B

(a)

3

1 (b)

2 B

3

Figure 35.2 Interaction plots of factors A and B.

To illustrate, suppose we have two factors A and B with levels a and b, respectively. In a full-factorial experiment, there would be ab total combinations, which are the treatments of interest. We would be interested in differences among the levels of factor A, the differences among the levels of factor B, and whether there exists a possible interaction between the two factors. An interaction between factors can be demonstrated graphically. Figure 35.2 represents the interaction plots of two factors A and B, where A has two levels (a = 2) and B has three levels (b = 3). Figure 35.2a indicates that there is no significant interaction between factors A and B. Notice that as we move across the levels of factor B, the levels of factor A maintain identical patterns. Figure 35.2b on the other hand indicates a significant interaction between factors A and B. Notice that by changing from level 1 to level 2 of factor A, the response is quite different. By changing the level of factor A, and keeping factor B at level 2, for example, the response has changed. When analyzing two-factor experiments, the effects to be analyzed are the main effects of factor A, the main effects of factor B, and the interaction between them.

EXAMPLE 35.22 Reconsider the biodiesel fuel example given earlier; now a second factor is of interest. Along with catalyst, the temperature of the water bath for the process is also of interest. There are two temperatures, 30°C and 60°C. Two replicates of each combination of catalyst and temperature are recorded, with the following results: Catalyst (%)

Temperature

30 60

0.6

1.0

1.4

75.22, 76.81 77.01, 75.39

83.10, 79.55 75.33, 72.67

69.24, 71.64 72.00, 74.57

In this study: • Some questions of interest are: Does the amount of catalyst have a significant effect on conversion rate? Does temperature have a significant Continued



Continued

Part VI.D.5

effect on conversion rate? Is there a significant interaction between temperature and the amount of catalyst? • Catalyst and temperature are the factors of interest or independent variables. • The factor “catalyst” has three levels (0.6 percent, 1.0 percent, and 1.4 percent). The factor “temperature” has two levels (30°C and 60°C). • Conversion rate is the response of interest (or dependent variable). • There are two replicates (n = 2) for each combination of catalyst and temperature.

Sums of Squares, Mean Squares, and the ANOVA Table Suppose we have two factors of interest, A and B, with a and b levels, respectively. Furthermore, suppose there are n replicates for each combination ab. The total sum of squares (SST) can be calculated for the two-way table and it can be partitioned into four sources of variability: SST = SSA + SSB + SSAB + SSE where SSA is the sum of squares for factor A, SSB is the sum of squares for factor B, SSAB is the sum of squares for the interaction AB, and SSE is the error sum of squares. The mean squares can be calculated and an ANOVA table created (see Table 35.3). The numerator and denominator degrees of freedom needed to find the appropriate critical value or calculate the p-value will vary depending on which factor is being tested. Consider factor A: the correct degrees of freedom needed to find the appropriate critical value are (a – 1), ab(n – 1) (that is, the degrees of freedom for the numerator of F0 and the degrees of freedom for the denominator of F0). As with the one-way ANOVA, the F-statistic measures the ratio between the effect

Table 35.3

Two-way ANOVA table.


df

SS

MS

Factor A

a–1

SSA

MSA

Factor B

b–1

SSB

MSB

(a – 1)(b – 1)

SSAB

MSAB

Error

ab(n – 1)

SSE

MSE

Total

abn – 1

SST

AB interaction

F

p-value

F0 =

MS A MS E

P(F > F0)

F0 =

MS B MS E

P(F > F0)

F0 =

MS AB MS E

P(F > F0)



Part VI.D.5


and the experimental error. If the variation due to the effect is a sufficiently large multiple of the error, the effect is considered statistically significant. Using p-values, this would mean reject the null hypothesis if p-value < a.

EXAMPLE 35.23 Reconsider the conversion rate example with two factors of interest, catalyst and temperature. The resulting ANOVA table is shown in Table 35.4. Using p-values, we would conclude that the amount of catalyst has a significant affect on the conversion rate (p-value = 0.009). But there is insufficient evidence to conclude that temperature has a significant affect on the conversion rate (p-value = 0.209). Finally, it appears that there is a significant interaction between catalyst and temperature (p-value = 0.016). The interaction plot is shown in Figure 35.3. There is a clear indication that a significant interaction exists between temperature and catalyst. If the goal is to maximize conversion rate, it appears that 1.0 percent catalyst and a temperature of 30°C would be a good choice.

Table 35.4 ANOVA table for conversion rate example with two factors of interest. Source of variability

df

SS

MS

F

p-value

Catalyst

2

72.104

36.0520

11.63

0.009

Temperature

1

6.149

6.1490

1.98

0.209

Catalyst × temperature interaction

2

55.635

27.8174

8.97

0.016

Error

6

18.598

3.0996

Total

11

152.486

82

Temperature 30 60

Mean response

80 78 76 74 72 70 0.6

1.0 Catalyst

1.4

Figure 35.3 Interaction plot for temperature and catalyst.



Number of Replicates As a final note on the two-way analysis of variance, it is important to realize that if each combination of the factors consists of only one observation (n = 1), you can not estimate the two-factor interaction. When n = 1, there will not be enough degrees of freedom left over for error, the important quantity MSE can not be estimated, and MSE is needed to calculate the test statistic. If it is acceptable to only estimate the main factors (A and B), then the interaction is removed (not tested) and the SSAB and degrees of freedom for the interaction are moved into error. If the interaction is possibly important, then it is recommended that at least two replicates for each combination of factors are collected.

Part VI.D.6

The details of the general notation and formulas will not be presented here, but the reader is encouraged to see Devore (2007), Montgomery and Runger (2006), Montgomery, Runger, and Hubele (2006), or Vining and Kowalski (2006).

6. CONTINGENCY TABLES

Define, construct, and use contingency tables to evaluate statistical significance. (Analyze) Body of Knowledge VI.D.6

In this section, a test concerning count data will be presented. Suppose a sample of n items has been collected and each item can be classified into two different categories at the same time. Data that can be classified according to two different criteria (or factors) can be displayed in a two-way contingency table. In cases such as this, it may often be of interest to determine whether the two categories are statistically independent of one another. For example, consider the population of high school graduates. We may want to determine if the hourly wage for an entry-level job is independent of graduating from high school.

Description Suppose there are r levels of factor 1 and c levels of factor 2. Each criterion can have several different levels. An r × c contingency table could be written as: Columns

Rows

1 2 : r

1

2

...

c

O11 O21 : Or1

O12 O22 : Or2

... ... ... ...

O1c O2c : Orc



Part VI.D.6

where The r rows represent the levels of the first factor. The c columns represent the levels of the second factor. Oij represents the number of observations that fall into category i of factor 1 and category j of factor 2, where i = 1, 2, . . . , r and j = 1, 2, . . . c.

EXAMPLE 35.24 A company operates two machines on three different shifts. The company wants to determine if machine breakdowns that occur during operation are independent of the shift on which the machine is used. The data are recorded in a 2 × 3 contingency table. Shift Machine

1

2

3

1

30

40

20

2

30

40

10

In this study: • r = 2, the number of machines • c = 3, the number of shifts • Oij represents the number of breakdowns that occur on the ith machine when used on the jth shift. For example O12 = 40. Therefore, 40 breakdowns have been recorded on machine 1 when it was used on the second shift.

We are interested in determining whether the two factors are independent of one another.

Hypothesis Test for Independence The expected frequencies are calculated based on the assumption that the two factors of interest are independent of one another. Denote the expected frequencies as Eij (for i = 1, 2, . . . , r and j = 1, 2, . . . , c). Calculate the expected frequencies for each entry in the contingency table using Eij = naibj, where • n = total number of observations; that is, n = O11 + O12+ + . . . + Orc • ai is the probability that a randomly selected observation will fall into the ith category of factor 1 (the row factor) and is found using the following formula: c

ai =

∑O j=1

ij

n



(Summing over the columns for the ith row)

r

bj =

∑O

ij

i=1

Part VI.D.6

• bj is the probability that a randomly selected observation will fall into the jth category of factor 2 (the column factor) and is found using the following formula:

n

(Summing over the rows for the jth column) If the two factors are independent, then we would expect the observed frequency and the expected frequency for each cell to be similar. This assumption can be tested using the test statistic r

c

c o2 = ∑ ∑

(Oij − Eij )2 Eij

i=1 j=1

,

which follows a chi-square distribution with (r – 1)(c – 1) degrees of freedom. The test statistic can be compared to a critical value from the chi-square distribution with (r – 1)(c – 1) degrees of freedom and a significance level a. The critical value is 2 denoted c a,(r–1)(c–1) . If our test statistic is greater than the critical value, we say there is enough evidence to conclude that the two factors are not independent. The test is valid as long as the expected frequency of each cell is at least five.

EXAMPLE 35.25 A company operates two machines on three different shifts. The company wants to determine if machine is independent of shift. The data are recorded in a 2 × 3 contingency table. Shift Machine

1

2

3

1

30

40

20

2

30

40

10

Solution: Calculate the expected frequency for each cell, where n = 30 + 40 + 20 + 30 + 40 + 10 = 170. The row probabilities (probabilities associated with the machines) are Machine 1: a 1 = (30 + 40 + 20)/170 = 0.53 Machine 2: a 2 = (30 + 40 + 10)/170 = 0.47. The column probabilities (probabilities associated with the shifts) are Machine 1: b 1 = (30 + 30)/170 = 0.353 Continued



Part VI.D.6

Continued

Machine 2: b 2 = (40 + 40)/170 = 0.47 Machine 3: b 3 = (20 + 10)/170 = 0.177. The expected frequencies for all six cells are E 11 = na 1b 1 = 170(0.53)(0.353) = 31.8

E 21 = na 2 b 1 = 170(0.47)(0.353) = 28.2

E 12 = na 1b 2 = 170(0.53)(0.47) = 42.35

E 22 = na 2 b 2 = 170(0.47)(0.47) = 37.55

E 13 = na 1b 3 = 170(0.53)(0.177) = 16

E 23 = na 2 b 3 = 170(0.47)(0.177) = 14.

The expected frequencies are summarized in the following table: Shift Machine

1

2

3

1

31.8

42.35

16

2

28.2

37.55

14

The test statistic is c 02 = ∑ ∑

(Oij − Eij )2 Eij

( 30 − 31 . 8 )2 ( 40 − 42 . 3 5)2 ( 20 − 16 )2 ( 30 − 28 . 2)2 ( 40 − 37 . 5 5)2 ( 10 − 14 )2 + + + + + 31 . 8 42 . 35 16 28 . 2 37 . 55 14 = 2 . 65 .

=

2 With a = 0.05 and degrees of freedom (r – 1)(c – 1) = 2, the critical value is c a,(r–1)(c–1) = 2 c 0.05,2 = 5.99. Since 2.65 < 5.99, the hypothesis of independence can not be rejected. Breakdown of a particular machine appears to be independent of which shift is using the machine.



Chapter 36 Part VI.E.1

E. Relationships Between Variables

T

his chapter covers three kinds of relationships between variables: linear regression, simple linear correlation, and time-series analysis.

1. LINEAR REGRESSION

Calculate the regression equation for simple regressions and least squares estimates. Construct and interpret hypothesis tests for regression statistics. Use regression models for estimation and prediction, and analyze the uncertainty in the estimate. [Note: Non-linear models and parameters will not be tested.] (Analyze) Body of Knowledge VI.E.1

Linear regression models are important statistical tools developed to relate two or more variables of interest. This relationship often takes the form of a linear equation or linear model. In this section, simple linear regression is presented. Simple linear regression is the situation where there are exactly two variables: • One independent variable (often denoted by x) • One dependent variable (often denoted by y)

EXAMPLE 36.1 When data have been collected relating two variables, it is often useful to find an equation that models the relationship. Then the value of the dependent variable can be predicted for a given value of the independent variable. For example, suppose a chemical engineer is investigating the relationship between the operating temperature of a Continued

473



Part VI.E.1

Continued

process and product yield. In this case, it might be useful to control the operating temperature (independent variable) in order to control or predict yield (dependent variable). For this example, eight readings are taken, although in an actual application more data would be desirable. Temperature, °C (x) Yield, % (y)

115

125

135

145

155

165

175

185

62

64

69

77

78

81

82

88

The first step in the investigation is to plot the data, as in Figure 36.1, to determine if it seems reasonable to approximate it with a straight line.

90 85

Yield

80 75 70 65 60 110

Figure 36.1

120

130

140 150 160 Temperature

170

180

190

Scatter plot of temperature and yield.

Although a perfect straight line can not be drawn through these points in Example 36.1, the trend looks linear. The next step is to find an equation that best fits the data. Before creating regression lines for particular problems, some assumptions and basic definitions must be presented.

Notation and Definitions Reconsider the scatter plot in Figure 36.1. The scatter plot indicates that the two variables may be linearly related. This is indicated by the fact that the observations fall approximately along a straight line. A simple linear regression model is one that models a linear relationship between the response of interest y and an independent (explanatory or regressor) variable x: y = b 0 + b1x + e


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 36: E. Relationships Between Variables 475

Definitions. Let x1, x2, . . . , xn represent real values of the independent variable (also called an explanatory variable) and y1, y2, . . . , yn represent real values of the dependent variable (also called the response). The sample consists of n pairs of data (x1, y1), (x2, y2), . . . , (xn, yn). The general model given above can be written in terms of the individual observations:

Part VI.E.1

where b 0 and b1 are called regression coefficients and e represents a random error term (recall that the data will rarely lie exactly along a straight line, so when a straight line is fit to the data, there will be some error).

yi = b 0 + b 1 xi + e i , for i = 1, 2 ,..., n For the simple linear regression model: • The coefficients b 0 and b1 are parameters that define the mathematical relationship between the independent and dependent variables. b 0 is the intercept and b1 is the slope. • The intercept is the value of y when x = 0. This is the height at which the regression line crosses the y-axis. • The slope represents the change in the response for every one unit change in the independent variable x. Given a series of values for an independent variable x and the corresponding dependent variable y, we can calculate point estimates for b 0 and b1. The point estimates are denoted b0 and b1. Once the point estimates are obtained, a fitted regression line can be given by yˆ i = b0 + b1 xi where yˆ i is the predicted value of the response for a given value of the independent variable.

Estimating the Parameters a 0 and a 1 The statistics b0 and b1 need to be calculated in such a way that the resulting fitted line will provide predicted values that will be close to the actual value for each value of x. One method for calculating b0 and b1 is based on minimizing the error between the actual value of y and the predicted value of y for each pair of data, that is, ei = yi – yˆ i. This is an appropriate method since a goal of simple linear regression is to make predictions after fitting a model between the independent variable and the response. A method frequently employed is the least squares method. The formulas for finding b1 and b0 are as follows: b1 =

Sxy Sxx

b0 = y − b1 x



where

Part VI.E.1

n

Sxy =

∑ ( x − x )( y i

i=1

− y)

n n

Sxx =

i

∑(x i=1

i

− x)

n

= ∑ xi yi −

⎛ n ⎞⎛ n ⎞ ⎜⎝ ∑ xi ⎟⎠ ⎜⎝ ∑ yi ⎟⎠

2

n

n

= ∑ xi2 −

⎛ n ⎞ ⎜⎝ ∑ xi ⎟⎠ i=1

n

i=1

i=1

i=1

n

i=1

2

.

These are often referred to as the least squares estimates. These formulas result in estimates that will give us a best-fitting line. By best fitting, we mean that the line with these estimates for the coefficients will result in the smallest error sum of squares.

Error Sum of Squares Let ei represent the error associated with the ith observation ei = yi – yˆ i for i = 1, 2, . . . , n (a realized value of the error is referred to as a residual). We want a fitted line that will minimize the n errors as much as possible. More specifically, we want to find the fitted regression line that will minimize the quantity n

∑e i=1

2 i

(the error sum of squares). The formulas for b0 and b1 given previously result in a fitted line that will make this quantity as small as possible. In fact, there are no other estimates of b 0 and b1 that will result in a smaller error sum of squares!

EXAMPLE 36.2 Reconsider the yield example given earlier. Temperature is the independent variable x and yield is the response y. We want to fit a regression line relating temperature to yield. Temperature, °C (x) Yield, % (y)

115

125

135

145

155

165

175

185

62

64

69

77

78

81

82

88

The necessary calculations for b 1 and b 0 are: n=8 8

∑x i=1

i

= 115 + 125 + ... + 185 = 1200

8

∑x

2 i

= 1152 + 1252 + ... + 1852 = 184 , 200

i=1

Continued



Continued 8

i

n

∑x y

= 62 + 64 + ... + 88 = 601

i

i=1

i=1

x = 150

i

= 115(62) + 1 2 5 ( 64 ) + ... + 185 ( 88 ) = 91, 695

y = 75 . 13

Calculate b 1 and b 0 :

Sxy

⎛ n ⎞⎛ n ⎞ ⎜⎝ ∑ x i ⎟⎠ ⎜⎝ ∑ y i ⎟⎠ n ( 1200 )(601) = 1545 i=1 = ∑ xi y i − i=1 = 911, 695 − n 8 i=1

Part VI.E.1

∑y

2

⎛ n ⎞ ⎜⎝ ∑ x i ⎟⎠ ( 1200 )2 = 4200 Sxx = ∑ x i2 − i = 1 = 184 , 200 − n 8 i=1 n

b1 =

Sxy Sxx

=

1545 = 0 . 37 4200

b0 = y − b1x = 75 . 13 − 0 . 37 ( 1 5 0 ) = 19 . 63 The final fitted regression line is then yˆ = 19.63 + 0.37x. The fitted regression line is plotted along with the original data in Figure 36.2. 90 85

Yield

80 eˆ4

75

{

70 65 60 110

120

130

140 150 160 Temperature

170

180

190

Figure 36.2 Scatter plot and fitted regression line for the yield data.

The fitted regression line represents the predicted value yˆ for each value of x. Graphically, the residuals mentioned earlier are the vertical differences between the actual value of y and the predicted value of y for each value of x. See Figure 36.2. The vertical line represents the residual for x4 = 145 (eˆ 4 = y4 – yˆ 4 = 77 – 73.28 = 3.72).



Part VI.E.1

Hypothesis Testing in Simple Linear Regression Fitting a simple linear regression model involves a sample of data. As a result there will naturally be some error in the estimates of the coefficients, b 0 and b1. Recall that when we are estimating a population mean with a sample mean there is some variability in this estimate. The same is true for the point estimates of the coefficients b0 and b1. The expected value and variance of the point estimate of b 0 are E(b0 ) = b 0 ⎡ 1 x2 ⎤ V (b0 ) = s 2 ⎢ + ⎥. ⎣ n Sxx ⎦ The expected value and variance for the point estimate of b1 are E(b1 ) = b 1 V (b1 ) =

s2 Sxx

where Sxx was defined previously and s 2 is the error variance (also referred to as the process variability). An estimate of s 2 is sˆ 2 =

SSE n− 2

where n – 2 is the error degrees of freedom and SSE represents the error sum of squares defined previously: n

n

i=1

i=1

SSE = ∑ ( yi − yˆ i )2 = ∑ ei2 The standard errors for b0 and b1 would be ⎡ 1 x2 ⎤ s.e.(b0 ) = V (b0 ) = s 2 ⎢ + ⎥ ⎣ n Sxx ⎦ s.e.(b1 ) = V (b1 ) =

s2 . Sxx

An important test in simple linear regression is a test on the coefficient b1. In particular, a test on the significance of regression would involve testing b1 = 0. If b1 = 0, then y = b 0 + b 1x + ε = b 0 + ε, which indicates no significant linear relationship between x and y.



Hypothesis Test on the Slope a 1 . A t-test can be used to test for significance of regression. The hypotheses of interest are

Part VI.E.1

H0: b1 = 0 Ha: b1 ≠ 0. The test statistic would be t0 =

b1 − 0 sˆ 2 Sxx

.

A large value of t0 would lead to rejection of the null hypothesis. An appropriate critical value is ta/2,n–2, found from the t table in Appendix P. If a p value is calculated, the null hypothesis would be rejected if the p value is small (p value < a ) and would indicate a significant linear relationship between x and y. Hypothesis Test on the Intercept a 0 . A t-test can also be conducted on the intercept b 0. The hypotheses of interest are H0: b 0 = 0 Ha: b 0 ≠ 0. The test statistic would be t0 =

b0 − 0 ⎡ 1 x2 ⎤ sˆ ⎢ + ⎥ ⎣ n Sxx ⎦

.

2

A large value of t0 would lead to rejection of the null hypothesis. An appropriate critical value is ta/2,n–2 found from the t table in Appendix P. If a p value is calculated, the null hypothesis would be rejected if the p value is small (p value < a ). The calculations do not need to be done by hand. A statistical software package can be used to carry out all the necessary calculations.

EXAMPLE 36.3 Reconsider the yield and temperature example given earlier. The hypotheses of interest are H 0 : b1 = 0 versus Ha: b1 ≠ 0 and H 0 : b 0 = 0 versus Ha: b 0 ≠ 0. The output from a particular statistical package for this problem is: Predictor Constant x

Coef 19.946 0.36786

SE Coef 4.735 0.03120

T 4.21 11.79

P 0.006 0.000 Continued



Part VI.E.1

Continued

The output can be interpreted as follows: • The row “Constant” is the hypothesis test on the intercept: H 0 : b 0 = 0 versus Ha: b 0 ≠ 0. • The row “x” is the hypothesis test on the slope: H 0 : b1 = 0 versus Ha: b1 ≠ 0. • The column labeled “Coef” contains the point estimates for each parameter, that is, b 0 = 19.946 and b 1 = 0.036786. • The column labeled “SE Coef” provides the standard error of each estimate, that is, s.e.(b 0) = 4.735 and s.e.(b 1) = 0.0312. • The column labeled “T” represents the test statistic for the intercept and slope; t 0 = 4.21 (test on the intercept) and t0 = 11.79 (test on the slope). • The last column, labeled “P,” contains the p value for each parameter. The p value for testing b1 = 0 is 0.000. Since this value is small, we can reject the hypothesis that b1 = 0 and conclude that the slope is not zero. That is, there appears to be a statistically significant linear relationship between temperature and yield. The test on the intercept b 0 = 0 also indicates that the intercept is significant. If we fail to reject the null hypothesis b 0 = 0, it is often left to the practitioner to determine if it makes practical sense to leave the intercept in the model.

100(1 – `)% Confidence Intervals on the Slope and Intercept. The 100(1 – a )% two-sided confidence interval on the intercept is given by b0 − ta /2 ,n− 2 s.e.(b0 ) ≤ b 0 ≤ b0 + ta /2 ,n− 2 s.e.(b0 ). The 100(1 – a )% two-sided confidence interval on the slope is given by b1 − ta /2 ,n− 2 s.e.(b1 ) ≤ b 1 ≤ b1 + ta /2 ,n− 2 s.e.(b1 ).

EXAMPLE 36.4 For the yield and temperature example, we wish to construct 95 percent confidence intervals on the slope and intercept. In this case, the value t a/2,n–2 = t 0.025,6 = 2.447 and the resulting confidence intervals are b0 − ta / 2 ,n− 2s.e.( b0 ) ≤ b 0 ≤ b0 + ta / 2 ,n− 2s.e.( b0 ) 1 9 . 96 − 2 . 447( 4 . 735) ≤ b 0 ≤ 19 . 96 + 2 . 447( 4 . 735) 8 . 3 7 ≤ b 0 ≤ 31 . 55 and

b1 − ta / 2 ,n− 2s.e.( b1 ) ≤ b 0 ≤ b1 + ta / 2 ,n− 2s.e.( b1 ) 0 . 37 − 2 . 447( 0 . 0312) ≤ b 1 ≤ 0 . 37 − 2 . 447( 0 . 0312) 0 . 2 9 ≤ b 1 ≤ 0 . 47 . Continued



Continued

Part VI.E.1

The confidence intervals do not contain zero, so we have evidence to indicate that the slope and intercept are both nonzero. Again, since the 95 percent confidence interval on b1 does not contain zero, we have evidence to indicate that there is a significant linear relationship between temperature and yield.

The Analysis of Variance Approach The analysis of variance (ANOVA) can also be used to test for significance of regression. The null and alternative hypotheses of interest are H0: b1 = 0 and Ha: b1 ≠ 0. Note that only the test on the slope will determine significance of regression. The total sum of squares (SST) is a measure of the total variability. SST can be partitioned into two sources of variability: the regression line we have fit and error. This is similar to the total sum of squares discussed in Chapter 35. The error sum of squares (also referred to as the residual sum of squares) defined in this chapter is a measure of the unexplained variability in the responses y. The variability due to the regression model that we have fit is the regression sum of squares (SSR). The partition is SST = SSR + SSE where n

SST = Syy = n

∑( y i=1

n

(

SS R = ∑ yˆ i − y i=1

− y) i

)

n

= ∑ yi2 −

⎛ n ⎞ ⎜⎝ ∑ yi ⎟⎠

i=1

2

i=1

n

2

and n

n

i=1

i=1

SS E = ∑ ( yi − yˆ i )2 = ∑ ei2 . It is desirable to have the regression sum of squares be large in comparison to the error sum of squares. A large value of SSR would indicate that most of the variability in the response can be explained by the regression model that has been fit. As in Chapter 35, we have to take into account the sample size and adjust the sum of squares using the appropriate degrees of freedom. An ANOVA table can be constructed (see Table 36.1).

Table 36.1

ANOVA table for testing significance of regression.

Source

df

SS

MS

F

p value

Regression

1

SSR

MSR = SSR/1

F0 = MSR/MSE

P(F > F0)

Error

n–2

SSE

MSE = SSE/(n – 2)

Total

n–1

SST



Part VI.E.1

If critical values are used, we would reject the null hypothesis if F0 > Fa,1,n–2. The critical value is found from the F table in Appendix H.

EXAMPLE 36.5 The ANOVA table for the yield and temperature example is given below. The null and alternative hypotheses are H 0 : b1 = 0 Ha: b1 ≠ 0. Source

df

SS

MS

F

p-value

Regression

1

568.34

568.34

138.98

0.000

Error

6

24.54

4.09

Total

7

592.87

Since the p value is zero, we would reject the null hypothesis and conclude again that there is a statistically significant linear relationship between yield and temperature. If critical values are used, the critical value is F 0.05, 1, 6 = 5.99 (assuming a 0.05 level of significance). Since 138.98 > 5.99, we again reject the null hypothesis.

The analysis of variance is useful not only for testing the significance of regression, but also provides an estimate of sˆ 2. Specifically, sˆ 2 = MSE. In this problem sˆ 2 = MSE = 4.09.

Assumptions in Regression Analysis There are a number of assumptions in linear regression. For the most part, the least squares approach, and the results or conclusions drawn from the data, are fairly robust to these assumptions, that is, it takes a substantial deviation from the norm to affect the results. The assumptions for the least squares approach to regression analysis are: 1. The errors ei are independent. 2. The errors ei are normally distributed with mean zero. 3. The errors ei have constant variance s 2. The assumptions can be checked using residual analysis. Residuals plotted against the fitted values yˆ or against the independent variable x can provide some information about the validity of the constant variance assumption. A normal probability plot of residuals can be used to assess the assumption of normality. The independence assumption can be verified by examining a plot of the residuals against the time sequence—if the time sequence is known. With the exception of the normal probability plot of the residuals, all residual plots should exhibit no obvious patterns in the residuals. If the residuals fall along a straight line in the



Prediction of Observations

Part VI.E.2

normal probability plot, then the normality assumption is assumed to be valid. Caution should be used when interpreting residual plots for small sets of data. For small sample sizes, patterns on residual plots can often occur by chance. Keep in mind that the least squares method is fairly robust to slight departures from these assumptions.

The fitted regression model is often used to make predictions of new or future observations for the response. Let x0 be a value of the independent variable. The point estimator of the new value, y0, is given by yˆ 0 = b0 + b1x0. For example, suppose we wish to predict the yield for a temperature of 150 degrees Celsius. The predicted value of the yield would be: yˆ 0 = 19 . 9 + 0 . 37 x0 = 19 . 9 + 0 . 37(150) = 75 . 4 %. Because the value is a single point estimate calculated from sample data, there is variability or error in this prediction. It is sometimes of interest to construct an interval estimate for a future observation. A prediction interval provides a measure of the estimate and error of prediction. For complete details on prediction intervals see Devore (2007), Montgomery and Runger (2006), Montgomery, Runger, and Hubele (2006), Neter, Kutner, Nachtsheim, and Wasserman (1996), or Vining and Kowalski (2006).

2. SIMPLE LINEAR CORRELATION

Calculate the correlation coefficient and its confidence interval, and construct and interpret a hypothesis test for correlation statistics. [Note: Serial correlation will not be tested.] (Analyze) Body of Knowledge VI.E.2

Correlation measures the strength of the linear relationship between two variables. A linear relationship exists between two variables if as one variable increases the other increases or decreases. Graphically, this will be seen if the values of the two variables plot along a straight line. Recall the discussion on the bivariate normal distribution given in Chapter 33. One of the parameters that defined



Part VI.E.2


the bivariate normal pdf was the population correlation coefficient r. The population correlation coefficient is often unknown and can be estimated using sample data. Suppose x and y are jointly normally distributed random variables. The sample correlation coefficient, denoted r, can be used as the estimate of the population correlation coefficient r. The sample correlation coefficient is r=

Sxy Sxx Syy

where n

Sxy

=

∑ ( x − x )( y i=1

i

− y) i

n

=

∑(x i=1

i

n n

Syy

=

− x)

2

∑( y i=1

i

− y)

n

n

= ∑ xi2 −

n

⎛ n ⎞ ⎜⎝ ∑ xi ⎟⎠

n

= ∑ yi2 − i=1

2

i=1

n

i=1

2

i=1

i=1

i=1

n

Sxx

n

= ∑ xi yi −

⎛ n ⎞⎛ n ⎞ ⎜⎝ ∑ xi ⎟⎠ ⎜⎝ ∑ yi ⎟⎠

⎛ n ⎞ ⎜⎝ ∑ yi ⎟⎠ i=1

n

2

.

The sample correlation coefficient r measures the strength of the linear relationship and has the following properties: • −1 ≤ r ≤ 1; the closer the value is to −1 or 1, the stronger the linear relationship. – If r is negative, that indicates that as one variable is increasing, the other is decreasing. – If r is positive, that indicates that both variables are increasing or both variables are decreasing. • If r = 0, then there is no linear relationship between the two variables. • r has no units attached to it, such as pounds, inches, feet, and so on. Figure 36.3 illustrates two variables x and y. Figure 36.3a displays two random variables that are positively correlated. In this case, the value of r is positive and near 1. Figure 36.3b displays two random variables that are negatively correlated. In this case the value of r is negative and near –1. Figure 36.3c displays two variables that are not linearly related at all. In this situation, there does appear to be some relationship between x and y, but it is not linear. Therefore, the correlation coefficient r would be zero.



90

85

85

80

80

75

75

Y

90

70

70

65

65

60

Part VI.E.2

Y

Chapter 36: E. Relationships Between Variables 485

60 110

120

130

140

150

160

170

180

190

110

120

130

140

150

X

X

(a)

(b)

160

170

180

190

90 85

Y

80 75 70 65 60 0

10

20

30

40

50

60

X

(c)

Figure 36.3 Various scatter plots for two variables x and y.

There is a direct relationship between the correlation coefficient and the slope of a model. The sign on the correlation coefficient is the same as the sign on the slope. Also, 2

⎛S ⎞ r = b1 ⎜ xx ⎟ . ⎝ SST ⎠ There is some subjectiveness in the interpretation of the correlation coefficient estimate. If −0.6 ≤ r ≤ 0.6 for example, there is not always agreement as to whether or not the association between the two variables is significantly correlated. A t-test on the population correlation coefficient can be conducted to determine the statistical significance of the correlation. For complete details on testing the significance of the population correlation coefficient see Devore (2007), Montgomery and Runger (2006), Montgomery, Runger, and Hubele (2006), or Neter, Kutner, Nachtsheim, and Wasserman (1996).



Part VI.E.2

EXAMPLE 36.6 Consider the temperature and yield example again. For this problem the sums of squares needed to determine the sample correlation coefficient are n

Sxy = ∑ x i y i −

⎛ n ⎞⎛ n ⎞ ∑ xi ⎠⎟ ⎝⎜ ∑ y i ⎠⎟ ⎝⎜ i=1

i=1

n

i=1

n

Sxx = ∑ x i2 −

⎛ n ⎞ ∑ xi ⎠⎟ ⎝⎜

i=1

n

Syy = ∑ y i2 − i=1

2

i=1

= 184 , 200

n ⎛ n ⎞ ∑ y i ⎠⎟ ⎝⎜ i=1

n

= 911, 695

2

= 45, 743

resulting in r=

Sxy Sxx Syy

=

91, 695 = 0 . 998 . 184 , 200 45, 743

The sample correlation coefficient of 0.998 indicates that there is a very strong positive linear relationship between temperature and yield.

Coefficient of Determination The coefficient of determination R 2 gives a measure of how adequate the current regression model is for a particular set of data. It is the proportion of the total variability in the response that can be explained by the regression line. The coefficient of determination is simply the square of the correlation coefficient r. (We often use the notation R 2 for this value even though correlation coefficient is denoted by lower case r.) Since –1 ≤ r ≤ 1 then 0 ≤ R2 ≤ 1. The coefficient of determination can be calculated by R2 =

SS R SS = 1− E . SST SST

EXAMPLE 36.7 For the yield and temperature problem, the coefficient of determination is R2 =

SSR 568 . 34 = = 0 . 959 . SST 592 . 87

We would conclude that approximately 95.9 percent of the total variability in the response (yield) can be explained by the regression model involving temperature.



Multiple Linear Regression

y = b 0 + b 1 x1 + b 2 x2 + ... + b k xk + e

Part VI.E.2

Multiple least-squares linear regression is an extension of simple linear regression. The response variable Y is a function of several independent variables x1, x2, . . . , xk. The equation for the multiple linear regression is

where b 0 represents the intercept. b1, b 2, . . . , b k are the coefficients of the independent variables, x1, x2, . . . , xk. e i represents the inherent variability in the process. A fitted regression model would be given as yˆ = b0 + b1 x1 + b2 x2 where b0 is an estimate for b 0, b1 is an estimate for b1, and b2 is an estimate for b 2.

EXAMPLE 36.8 An article in the Journal of Agricultural Engineering Research (2001, p. 275) describes the modeling of damage susceptibility of peaches to several independent variables. Two independent variables that are believed to impact peach damage are the height at which the peach is dropped (mm) and peach density (g/cm3). Data that are typical of this type of experiment are as follows: y

x1

x2

7.22 4.24 8.50 9.32 5.87 7.12 8.04 6.62 10.06 8.96

371.05 315.02 550.10 400.00 336.00 361.10 499.24 403.58 482.33 451.65

0.99 1.11 0.97 1.02 0.96 0.95 1.01 1.00 1.04 0.98

The multiple regression line for this set of data is (found using a statistical software package) yˆ = 5 . 4 + 0 . 016 x 1 − 4 . 48 x 2 where b 0 = 5.4, b 1 = 0.016, and b 2 = –4.48. T-tests were conducted on the parameters of interest b 0, b1, and b 2 , and it was determined that peach density (x 2) is insignificant (p value > 0.10). Regression analysis was Continued



Part VI.E.3

Continued

carried out again but this time relating only height dropped (x1) and damage (y). (Note that the intercept is also found insignificant, but the researchers determined that it is appropriate to leave it in.) The final fitted model is yˆ = 0 . 66 + 0 . 016 x 1. The fitted model may be useful for predicting damage, but further analysis is necessary. The coefficient of determination was calculated for the final fitted model and found to be 52.9 percent. That is, approximately 53 percent of the total variability in damage can be explained by the fitted regression line involving only drop height. Although peach density is not a significant variable for damage, there may be other independent variables that should be investigated. Residual analysis should also be done for this problem.

For complete details on multiple regression, see Devore (2007), Montgomery and Runger (2007), Montgomery, Runger, and Hubele (2006), Neter, Kutner, Nachtsheim, and Wasserman (1996), or Vining and Kowalski (2006).

Causal Relationship As a final note on regression analysis, finding a fitted regression line statistically significant does not imply a causal relationship between the independent and dependent variables. Two variables could be linearly related and have a very strong association, but this does not infer that one variable caused the change in the other variable. Causation could only be concluded if a designed experiment were conducted for a particular problem.

3. TIME-SERIES ANALYSIS Define, describe, and use time-series analysis including moving average, and interpret timeseries graphs to identify trends and seasonal or cyclical variation. (Analyze) Body of Knowledge VI.E.3

Time series analysis in mathematical statistics involves mathematical techniques for determining cycles and trends in data. The two specific tools discussed in this section are: • Moving average smoothing • Trend analysis Moving average smoothing and trend analysis are two methods of analyzing data. There are other methods, but those are beyond the scope of this discussion.



35

Part VI.E.3

Rate

30

25

20

15 2

Figure 36.4

4

6

8

10

12 14 Month

16

18

20

22

24

Run chart for NMCM rate.

Run Charts Run charts display a plot of data obtained on sequential samples taken from a process. This plot has an x-axis of sequence or time. The y-axis is that of the measurement taken on the sample. For example, the rate of not mission-capable equipment or systems due to maintenance issues (NMCM) is important to monitor. Figure 36.4 displays the monthly NMCM rates over a two-year period. Note that in this case we can see that there might be some sort of cycle present in the NMCM rate. The process may not have a random pattern of NMCM rates around the average rate. We have to know the scale or time period of the x-axis very well in order to correctly interpret the run chart. The control chart discussed in Chapter 37 is a special type of the run chart. The control chart has a center or central line (the mean) drawn in to facilitate our eyes seeing the data move back and forth across this line. When the measurement for one sample tends to be dependent on the measurement for the previous sample, these data are called autocorrelated. As will be shown in Chapter 37, one assumption necessary for control charts to be valid is that the observations are independently distributed. When autocorrelation is present in the data, standard control charts do not work well in monitoring the process. Suppose there are n measurements taken in some time sequence. A measure of sample autocorrelation is given by n− k

rk =

∑(x t=1

t

− x ) ( xt− k − x )

∑(x t=1

− x) t

2

, for k = 1, 2 ,..., K

where k represents the number of time periods between measurements x– is the average of all measurements



xt is the measurement taken at time t

Part VI.E.3

xt–k is the measurement taken at time t – k. For example, if it is believed that measurements taken one after another are autocorrelated, then k = 1. In many problems, we may need to compute rk for several values of k. Detecting autocorrelation can be accomplished using a number of methods. Two methods include moving average smoothing and trend analysis. Moving average smoothing involves smoothing the data over a short interval of time. In particular, consecutive observations in a series are averaged over a chosen window of time in order to remove as much noise as possible from the system. Trend analysis fits a general trend model to time series data and provides forecasts. Some models commonly fit include the linear, quadratic, exponential, and S-curve. Both methods work well when no seasonal component is present in the data. For complete details of time-series analysis see Montgomery, Jennings, and Kulahci (2008). For discussion of autocorrelation with respect to statistical process control see Montgomery (2009b).



Chapter 37 Part VI.F.1

F. Statistical Process Control (SPC)

T

his chapter covers eight aspects of statistical process control: objectives and benefits, common and special causes, selection of variable, rational subgrouping, control charts, control chart analysis, pre-control charts, and short-run SPC.

1. OBJECTIVES AND BENEFITS

Identify and explain objectives and benefits of SPC such as assessing process performance. (Understand) Body of Knowledge VI.F.1

Statistical process control (SPC) is quantitative problem solving, consisting of diagnostic techniques to assist in locating problem sources and prescriptive techniques to help solve problems. Many of these techniques are based on statistical principles. A process is any repeatable sequence of events or operations leading to either a tangible or intangible outcome. The use of SPC will show that a process is 1) in statistical control, that is, the process variation appears to be random or 2) out of statistical control, that is, the process exhibits nonrandom variation. SPC also makes it possible to determine whether or not the process is improving. SPC is a tool for communicating information to engineering, product operations, and quality control personnel. The principal elements of a successful SPC framework are analysis—to understand the process, methods—to measure the process, and leadership—to change the process. A number of benefits can be attributed to SPC. Continuous improvement and maintenance of quality and productivity can be achieved, and through the SPC process complexity can be reduced. By identifying and reducing process complexity, errors will be reduced and productivity improved through the substitution of sampling for 100 percent inspection. SPC also provides a common internal

491



Part VI.F.3

language for management, supervision, quality assurance/control, and product operations to discuss problems, solutions, decisions, and actions.

2. COMMON AND SPECIAL CAUSES

Describe, identify, and distinguish between these types of causes. (Analyze) Body of Knowledge VI.F.2

Every process has variation. The sources of process variation can be divided into two categories: special and common. Common cause variability is that which is inherent in the process and generally is not controllable by process operators. Examples of common causes include variation in raw materials and variation in ambient temperature and humidity. In the case of service processes, common causes typically include such things as variation in input data, variations in customer load, and variation in computer operations. Some authors refer to common cause variation as natural variation. Special causes of variation include unusual events that when detected can usually be removed or adjusted. Examples include tool wear, gross changes in raw materials, and broken equipment. Special causes are sometimes called assignable causes. A principal problem in process management is the separation of special and common causes. If the process operator tries to adjust a process in response to common cause variation, the result is usually more variation rather than less. This is sometimes called overadjustment or overcontrol. If a process operator fails to respond to the presence of a special cause of variation, this cause is likely to produce additional process variation. This is referred to as underadjustment or undercontrol. The principal purpose of control charts is to help the process operator recognize the presence of special causes so that appropriate action can be taken. Control charts are discussed in detail in the sections that follow.

3. SELECTION OF VARIABLE

Identify and select characteristics for monitoring by control chart. (Analyze) Body of Knowledge VI.F.3


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Chapter 37: F. Statistical Process Control 493

Part VI.F.4

When a control chart is to be used, a variable (or variables) must be selected for monitoring. In a new process, there may be many different quality characteristics to monitor. However, as the process becomes more stable, the number of monitored characteristics will most likely be reduced. Sometimes the variable of interest is the most critical dimension of the product. Contractual requirements with a customer sometimes specify the variable(s) to be monitored via a control chart. If the root cause of the assignable variation is known, an input variable, such as voltage or air pressure, may be monitored. It is possible to monitor several variables on separate control charts. But it is also useful to monitor two or more characteristics using a single control chart (multivariate control chart). Ultimately, the selection of the quality characteristic to be charted depends on experience and judgment.

4. RATIONAL SUBGROUPING

Define and apply the principles of rational subgrouping. (Apply) Body of Knowledge VI.F.4

The selection of samples is important in the construction of control charts. The method used to select samples for a control chart must be logical or rational. In general, rational subgrouping involves selecting samples such that if assignable causes of variation are present in the system, there should be a greater probability of variation between successive samples while the variation within the sample is kept small. Samples frequently consist of parts that are produced successively or consecutively by the same process, to minimize the within-sample variation. The next sample is chosen later so that any process shifts that have occurred will be displayed on the chart as between-sample variation. Choosing the rational subgroup requires care to make sure the same process is producing each item. There are instances where it is more appropriate to select the sample over the entire interval since the last sample was chosen. This approach to rational subgrouping is effective in detecting shifts that may occur between samples taken consecutively. The sample represents all units produced since the last sample was taken. In general, the subgroup is a random sample of units selected over the entire interval since the last subgroup was selected. Caution should be used when interpreting control charts where the subgroups are units randomly selected over an interval. It is possible to make even an out-ofcontrol process appear to be in control simply by increasing the interval between selected units.



Part VI.F.5

5. CONTROL CHARTS

Identify, select, construct, and use various – – control charts, including X –R, X range (ImR or XmR), moving average and moving range (MAMR), p, np, c, u, and CUSUM charts. (Analyze) Body of Knowledge VI.F.5

Control charts can be used for monitoring many quality characteristics: • Monitoring individual observations or subgroups for continuous data. • Control charts for continuous data in subgroups are used in pairs to monitor for changes or trends in location and dispersion. • Control charts for discrete data can be used to detect increases in the number of nonconforming units, nonconformities per unit, or proportions. Control charts are the most common tool for monitoring a quality characteristic of interest. Dr. Walter A. Shewhart introduced the concept of control charts in the 1920s. Because of his work, several control charts monitoring a single quality characteristic of interest are referred to as Shewhart control charts. In this section, control charts for variables data and attributes data will be presented. The control charts to be discussed are: • x– and R control charts • x– and s control charts • Individuals control charts • Fraction nonconforming control charts • Control charts for nonconformities For each of these control charts, an assumption that must be satisfied is that the data being monitored follow a normal distribution. The Shewhart control charts are sensitive to this assumption. If the normality assumption is violated, the overall performance of these charts can be very poor and result in incorrect signals.

Control Limits Control limits are calculated based on data from the process. Formulas for control limits and examples of each are given in this section. The formulas are repeated in Appendix B. Several constants are needed in the formulas. These appear as subscripted letters such as A2. The values of these constants are given in Appendix C. When calculating control limits it is prudent to collect as much data as practical.



Table 37.1

General notation for subgroup data. Measurements

1

x11, x21, . . . , xn1

2

x12, x22, . . . , xn2

3 . . . m

x– i x–

Ri R1

1

R2

x13, x23, . . . , xn3

x– 2 x–

3

R3

. . . x1m, x2m, . . . , xnm

. . . – xm

. . . Rm

m

x=

∑x i=1

m

Part VI.F.5

Subgroup, i

m

i

R=

∑R i=1

i

m

Many authorities specify at least 25 samples. The examples in the following sections use fewer samples for simplicity. It is desirable for the sample size to be held constant if possible.

Variables Control Charts The most commonly used control charts for variables (continuous) subgroup data are the x– and R charts and the x– and s charts. The x– chart monitors the mean of the process while the R chart and s chart monitor the process variability. The x– chart is used for monitoring of the process mean in conjunction with either the R chart or s chart and process variability. Suppose there are m subgroups each of size n chosen at random from a particular process (see Table 37.1). The sample mean and range for each subgroup are also given in Table 37.1. – The statistic x is the grand average and is the best estimate of the true process – mean m. R is the average range and will be used to estimate the process variability and construct control charts. The upper control limit (UCL), centerline (CL), and lower control limit (LCL) for the x– control chart are UCL = x + A2 R CL = x LCL = x − A2 R. The upper control limit (UCL), centerline (CL), and lower control limit (LCL) for the R control chart are UCL = D4 R CL = R LCL = D3 R.



Part VI.F.5


A2 , D3, and D4 are constants that depend on the sample size n. They can be found in Appendix C. Derivations of these constants can be found in Montgomery (2009b).

EXAMPLE 37.1 The turnaround time for CBC analysis from the laboratory to the emergency room at a local hospital is an important quality characteristic to be monitored. Turnaround times were recorded over 20 days in a one-month period. Four specimens were randomly selected per day and the turnaround times (in minutes) recorded. The times as well as the subgroup averages and ranges are given in Table 37.2. The grand average and average range are given in the last row of Table 37.2. From Appendix C with n = 4, we find A 2 = 0.729, D 3 = 0, and D 4 = 2.282. The control limits for the x– control chart are UCL = x + A2R = 71 . 39 + 0 . 729( 27 . 05) = 91 . 11 CL = x = 71 . 3 9 LCL = x − A2R = 71 . 39 − 0 . 729( 27 . 05) = 51 . 67 .

Table 37.2 Turnaround times for CBC analysis. Day

x1

x2

x3

x4

x– i

Ri

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

83 81 71 92 75 70 74 80 80 90 75 64 97 84 76 62 73 65 73 75

49 77 67 53 58 79 50 66 63 77 51 65 57 62 63 68 77 65 64 88

65 75 44 93 90 87 68 75 72 92 89 88 88 55 70 66 91 84 84 65

78 76 58 74 51 49 45 64 81 64 74 59 76 68 66 55 83 46 71 93

68.75 77.25 60.00 78.00 68.50 71.25 59.25 71.25 74.00 80.75 72.25 69.00 79.50 67.25 68.75 62.75 81.00 65.00 73.00 80.25

34 6 27 40 39 38 29 16 18 28 38 29 40 29 13 13 18 38 20 28

x– = 71.39

– R = 27.05 Continued



Continued

Part VI.F.5

The control limits for the R control chart are UCL = D4R = 2 . 282( 27 . 05) = 61 . 73 CL = R = 27 . 05 LCL = D3R = 0( 27 . 05) = 0 . The x– and R control charts for turnaround times are displayed in Figure 37.1. There are no points that plot outside the control limits on either chart. There also do not appear to be any obvious patterns on the x– control chart. The process appears to be in control. More discussion of interpretation of control charts is provided in Section 6 of this chapter.

UCL = 91.09

Sample mean

90 80

– X = 71.39

70 60

LCL = 51.68

50 2

4

6

8

10 12 Sample

14

16

18

20

UCL = 61.71

Sample range

60 45

– R = 27.05

30 15

LCL = 0

0 2

Figure 37.1

4

6

8

10 12 Sample

14

16

18

20

x– and R control charts for turnaround times.

x– and s Charts. Whenever possible, the sample standard deviation should be used instead of the range in estimating the process variability. When the sample size n is large, say n > 10, the sample standard deviation is a better estimate of the true process standard deviation than is the range. As n increases, the range R loses statistical efficiency and becomes less precise. The sample standard deviation is also a better estimator for the process standard deviation for nonconstant sample sizes. The x– and s control chart’s development is similar to development of the x– and R control chart. In this case, the subgroup standard deviation is calculated instead of the range. Suppose xi1, xi2, . . . , xin represent a sample of size n for any subgroup i. The formula for the sample standard deviation of subgroup i is



Part VI.F.5

n

∑ (x j=1

si =

− x )2

ij

n−1

.

The average standard deviation for all m subgroups is m

s=

∑s i=1

i

. m The control limits and centerline for the x– control chart are then UCL = x + A3 s CL = x LCL = x − A3 s . The control limits and centerline for the s chart are UCL = B4 s CL = s LCL = B3 s where A3, B3, and B4 are constants that depend on the sample size n. They can be found in Appendix C.

EXAMPLE 37.2 Reconsider the turnaround time data from the previous example. Instead of the range for each day (subgroup), the standard deviation is calculated. The subgroup averages will not change. The sample standard deviations and average standard deviations are given in Table 37.3. The control limits and centerline for the x– control chart are

UCL = x + A3 s = 71 . 39 + 1 . 628(11 . 97 ) = 90 . 88 CL = x = 71 . 3 9 LCL = x − A3 s = 71 . 39 − 1 . 628(11 . 97 ) = 51 . 90 . The control limits and centerline for the s chart are

UCL = B4 s = 2 . 266(11 . 97 ) = 27 . 12 CL = s = 11 . 97 LCL = B3 s = 0(11 . 97 ) = 0 where A 3 = 1.628, B 3 = 0, and B 4 = 2.266, from Appendix C with n = 4. The x– and s control charts are displayed in Figure 37.2. Continued



Continued

Turnaround times for CBC analysis.

Day

x1

x2

x3

x4

x– i

si

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

83 81 71 92 75 70 74 80 80 90 75 64 97 84 76 62 73 65 73 75

49 77 67 53 58 79 50 66 63 77 51 65 57 62 63 68 77 65 64 88

65 75 44 93 90 87 68 75 72 92 89 88 88 55 70 66 91 84 84 65

78 76 58 74 51 49 45 64 81 64 74 59 76 68 66 55 83 46 71 93

68.75 77.25 60.00 78.00 68.50 71.25 59.25 71.25 74.00 80.75 72.25 69.00 79.50 67.25 68.75 62.75 81.00 65.00 73.00 80.25

15.20 2.63 11.97 18.81 17.52 16.38 13.94 7.54 8.37 13.00 15.73 12.94 17.29 12.37 5.62 5.74 7.83 15.51 8.29 12.69

x– = 71.39

–s = 11.97

UCL = 90.87

Sample mean

90 80

– X = 71.39

70 60

LCL = 51.90

50 2

Sample standard deviation

Part VI.F.5

Table 37.3

4

6

8

10 12 Sample

14

16

18

20

30

UCL = 27.12

20 s– = 11.97

10

LCL = 0

0 2

Figure 37.2

4

6

8

10 12 Sample

14

16

18

20

x– and s control charts for CBC analysis turnaround times.

The process appears to be in control since there are no obvious trends or patterns and points plot within the control limits on both charts.



Part VI.F.5

Further interpretation of these and other control charts will be presented in Section 6 of this chapter.

Individuals Control Charts Many practical applications exist in which the subgroup consists of a single observation (n = 1). Examples include very slow processes or processes in which the measurement is very expensive to obtain, such as with destructive tests. An individuals control chart (I control chart) for variable data is appropriate for this type of situation. The individuals control chart uses the moving range of two successive subgroups to estimate process variability (see Montgomery [2009b] for a detailed discussion of moving range and individuals control charts in general). The moving range is given by MRi = |xi – xi–1|. For m subgroups of size n = 1 each, m – 1 moving ranges are defined as MR2 = |x2 – x1|, MR3 = |x3 – x2|, . . . , MRm = |xm – xm–1|. The average moving range is simply m

MR =

∑ MR

i

i= 2

m−1

.

Division is done by m – 1 since only m – 1 moving range values are calculated (there is no moving range for subgroup 1). Control charts are constructed for the individual observations (x chart) and the moving range of the subgroups (MR chart). The control limits and centerline of the x (or individuals) control chart are UCL = x + 3

MR d2

CL = x LCL = x − 3

MR d2

where d2 is a constant that depends on the number of observations used to calculate the moving range for each subgroup (that is, n = 2). Values for d2 can be found in Appendix C. The control chart for individuals is constructed by plotting the actual observation xi, the control limits, and the centerline against the subgroup (or time) order. The control limits and centerline for the moving range control chart are UCL = D4 MR CL = MR LCL = D3 MR



EXAMPLE 37.3 Packages of a particular instant dry food are filled by a machine and weighed. The weights (in ounces) for 15 successive packages have been collected and are displayed in Table 37.4. The engineer wishes to determine whether the filling process is indeed in control. The moving ranges are calculated using MRi = |xi – xi–1|. To illustrate, consider the first moving range at subgroup 2:

Part VI.F.5

where D3 and D4 are constants found in Appendix C for n = 2. The moving range control chart is constructed by plotting the m – 1 moving ranges, the control limits, and the centerline against the subgroup (or time) order.

MR 2 = |x 2 – x1| = |19.92 – 19.85| = 0.07 The remaining moving ranges are calculated accordingly and are given in Table 37.4. The control limits and centerline for the individuals chart with moving ranges of size 2 are UCL = 19 . 954 + 3

0 . 39 = 20 . 991 1 . 128

CL = 19 . 954 LCL = 19 . 954 − 3

0 . 39 = 18 . 917 . 1 . 128

The control limits and centerline for the moving range chart are UCL = 3 . 267( 0 . 39) = 1 . 274 CL = 0 . 39 LCL = 0( 0 . 39) = 0 .

Table 37.4 Weights for dry food packages. Bottle

Weight (x i )

Moving range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

19.85 19.92 19.93 19.26 20.36 19.96 19.87 19.80 20.40 19.98 20.17 19.81 20.21 19.64 20.15

— 0.07 0.01 0.67 1.10 0.40 0.09 0.07 0.60 0.42 0.19 0.36 0.40 0.57 0.51 —– M R = 0.39

x– = 19.954

Continued



The control charts for individual observations and for the moving range are displayed in Figure 37.3. Examining these control charts, the process does not appear to be out of statistical control.

UCL = 20.991

21.0 Individual value

Part VI.F.5

Continued

20.5 – X = 19.954

20.0 19.5 19.0

LCL = 18.917 1

2

3

4

5

6

7 8 9 10 Observation

11

12

13

14

15

UCL = 1.274

Moving range

1.2 0.9 0.6

—– MR = 0.39

0.3 0.0

LCL = 0 1

2

3

4

5

6

7 8 9 10 Observation

11

12

13

14

15

Figure 37.3 I and MR control charts for package weights.

It is important to note that the moving range control chart can not be interpreted in the same way as the R chart presented earlier, with respect to patterns or trends. Patterns or trends identified on the moving range chart do not necessarily indicate that the process is out of control. The moving ranges are correlated (recall MRi = |xi – xi–1|). There is a natural dependency between successive MRi values.

Attributes Control Charts Attributes control charts are used for discrete or count data. There are many scenarios where the quality characteristic of interest is simply a classification of the measurement into a single category. For example, manufactured products may be measured but only classified as defective/nondefective, conforming/ nonconforming, or pass/fail. Other situations may involve monitoring the number of nonconformities on an item. For example, billing statements may be examined for errors such as incorrect name, missing information, and incorrect amounts or type of service identified. Variables control charts are not appropriate for many



of these situations. Control charts for data that can be classified are attributes control charts. The following attributes control charts will be discussed:

• Number nonconforming control charts (np charts) • Control charts for nonconformities (c and u control charts)

Part VI.F.5

• Fraction nonconforming control charts (p charts)

Definitions and Notation. Explanation of important notation and definitions is in order before presenting the various control charts. For the fraction nonconforming control charts, the quality characteristic of interest can be placed into one of exactly two categories. These categories may be pass/fail, conforming/ nonconforming, and so on. For simplification, the term nonconforming will be used as a general reference regardless of the final categories to be used. The notation to be used is as follows: • n—number of items examined (lot size, sample size). • m—number of subgroups. • X—the number of nonconforming items found in the sample of size n, where X ≤ n. • p—probability that any one item of interest will be nonconforming. This parameter is often unknown and must be estimated. • pˆ —is the sample fraction nonconforming. By definition, pˆ =

X n

and is calculated for each of the m subgroups. • p– is the average fraction nonconforming. By definition, m

p=

∑ pˆ i=1

i

m

and is an estimate of p, defined above. The p Chart. The p chart is used to monitor the proportion nonconforming directly. The control limits and centerline (when p is unknown) are UCL = p + 3

p(1 − p ) n

CL = p LCL = p − 3

p(1 − p ) . n



Part VI.F.5


The control limits, centerline, and individual sample fraction nonconforming pˆ i are plotted against the subgroup number m. If any of the fraction nonconforming lie outside the control limits, the process is considered out of control. Patterns or trends would also be an indication of possible out-of-control situations.

EXAMPLE 37.4 A small bank collects data on the number of weekly account activities that are recorded in error. Over a 12-week period 1000 account activities are randomly selected and examined for the number that are in error. The bank would like to monitor the proportion of errors being committed, by establishing control charts. The fraction in error pî for each week must be computed. The fraction in error for each week is given in Table 37.5 for n = 1000. The average fraction in error p– is found to be 12

p=

∑ pˆ i=1

12

i

= 0 . 01042 .

The control limits and centerline for the p chart are p( 1 − p ) 0 . 01042( 1 − 0 . 01042) = 0 . 01042 + 3 = 0 . 020052 n 1 0 00 CL = 0 . 01042

UCL = p + 3

LCL = p − 3

0 . 01042( 1 − 0 . 01042) p( 1 − p ) = 0 . 00078 . = 0 . 010 4 2 − 3 1000 n

The resulting p chart is displayed in Figure 37.4. There is a single point that plots beyond the upper control limit. This point should be investigated to determine if it is truly an unusual point. If it is found to be unusual and the assignable cause identified, the point

Table 37.5

Account activities in error.

Week

Accounts in error

pî

1 2 3 4 5 6 7 8 9 10 11 12

6 11 4 10 5 30 9 8 12 7 12 11

0.006 0.011 0.004 0.010 0.005 0.030 0.009 0.008 0.012 0.007 0.012 0.011

Continued



Continued

Part VI.F.5

can be removed and the centerline and control limits recalculated. Suppose in this case that the cause for the outlier in week 6 was identified and a revised control chart constructed. The revised control chart is shown in Figure 37.5. Notice that the control limits and centerline have been updated while the fraction in error for week 6 is still plotted on the graph. On a revised control chart, the removed point is used only as a placeholder.

1

0.030

Proportion

0.025 0.020

UCL = 0.02005

0.015 p– = 0.01042

0.010 0.005

LCL = 0.00078

0.000 1

Figure 37.4

2

3

4

5

6 7 Week

8

9

10

11

12

p chart for accounts in error.

1

0.030

Proportion

0.025 0.020 UCL = 0.01741 0.015 0.010

p– = 0.00864

0.005 LCL = 0

0.000 1

Figure 37.5

2

3

4

5

6 7 Sample

8

9

10

11

12

Revised p chart for accounts in error.



Part VI.F.5

Table 37.6

Surgical site infection rates.

Month

Surgeries

Surgical infection

1

57

8

2

62

6

3

66

1

4

57

2

5

69

2

6

63

6

7

55

10

8

56

6

9

54

9

10

62

3

11

65

4

12

69

5

It is not necessary that the sample sizes be equal for all subgroups. For example, suppose surgeries that result in surgical site infections are monitored at a particular hospital. Suppose that the number of surgeries each month is examined and those resulting in surgical infections recorded. Typical data for a 12-month period are given in Table 37.6. The sample size is variable, and there are two ways to calculate the control limits: • Use the same formulas for the control limits given earlier, using the average sample size as an estimate for n. • Use the actual sample sizes and construct varying control limits. For the surgical infection rates, varying control limits were used. The control chart is displayed in Figure 37.6. The np Chart. The np chart is a variation of the p chart, with the actual number of nonconforming items plotted on the chart. The np chart and the p chart for the same problem will provide identical information. That is, if the p chart indicates that a process is out of control, then the np chart will also indicate that the same process is out of control. One of the reasons the np chart is an attractive alternative to the p chart is ease of interpretation. The average fraction nonconforming p– is the only value that must be estimated before constructing the control limits. The average fraction nonconforming can be found without having to calculate the sample fraction nonconforming values (pˆ i). For the np chart, the average fraction nonconforming can be calculated as



0.20 UCL = 0.1847

Part VI.F.5

Proportion

0.15

0.10

p– = 0.0844

0.05

0.00

LCL = 0 1

2

3

4

5

6 7 Month

8

9

10

11

12

Tests performed with unequal sample sizes

Figure 37.6

p chart for surgical site infection rate using varying sample sizes.

m

p=

∑X i=1

mn

i

.

The control limits and centerline are then UCL = np + 3 np(1 − p ) CL = np LCL = np − 3 np(1 − p ). The control limits, centerline, and the number of nonconforming items Xi are plotted against the subgroup. Interpretation of the chart is identical to that of the p chart.

EXAMPLE 37.5 Reconsider the accounts in error from the previous example. The average fraction in error was found to be p– = 0.01042. The control limits and centerline for the np control chart are UCL = np + 3 np( 1 − p ) = 1000( 0 . 01042) + 3 1000( 0 . 01042) ( 1 − 0 . 01042) = 20 . 05 CL = np = 100( 0 . 01042) = 10 . 42 L CL = np − 3 np( 1 − p ) = 1000( 0 . 01042) − 3 1000( 0 . 0104 2)( 1 − 0 . 01042) = 0 . 787 Continued



Part VI.F.5

Continued

The np control chart is displayed in Figure 37.7. As with the p chart, the np chart indicates that the process is out of statistical control.

1

30

Sample count

25 20

UCL = 20.05

15 n–p– = 10.42

10 5

LCL = 0.78

0 1

Figure 37.7

2

3

4

5

6 7 Week

8

9

10

11

12

np control chart for accounts in error.

Control Charts for Nonconformities. Control charts for nonconformities are similar to those for the number of nonconforming items, discussed in the previous subsection. The p chart and np chart represent the fraction of nonconforming items. When the variable of interest is the number of nonconformities per unit, the p and np charts are not appropriate. For p and np charts, we noted that the number of nonconforming units could not exceed the number of units being investigated in the subgroup, that is, X ≤ n. For monitoring nonconformities, there is no such restriction. In this case, nonconformities are counted per unit. There could be an infinite (countably infinite) number of nonconformities on a unit or units. For example, billing statements may be examined for errors such as incorrect name, missing information, incorrect amounts, or wrong type of service identified. More than one of these errors may occur on any one statement. Control charts for nonconformities are the c chart and the u chart. The c Chart. If the subgroup size n is constant from subgroup to subgroup, the c chart is an appropriate control chart for nonconformities. For the c chart: • n = number of units inspected, sample size (this can be size n = 1 or greater). • m = number of subgroups.



• X = number of nonconformities per unit inspected or per subgroup. • c– = average number of nonconformities:

Part VI.F.5

m

c=

∑X i=1

i

m

The control limits and centerline for the c chart are UCL = c + 3 c CL = c LCL = c − 3 c .

EXAMPLE 37.6 Billing statements for a local hospital are being examined for errors. Twenty billing statements are randomly chosen each day over a 24-day period and examined for missing information, incorrect amounts, and wrong type of service identified. The number of errors (nonconformities) is given in Table 37.7. The average number of errors is m

c=

Table 37.7

∑X i=1

m

i

=

4 + 18 + ... + 10 = 10 . 29 . 24

Errors on hospital billing statements.

Day

Number of errors

Day

Number of errors

1 2 3 4 5 6 7 8 9 10 11 12

4 18 14 7 7 8 16 6 10 12 9 8

13 14 15 16 17 18 19 20 21 22 23 24

10 13 3 12 17 13 9 17 9 9 6 10 Continued



Part VI.F.5

Continued

The control limits and centerline for the c chart are UCL = c + 3 c = 10 . 29 + 3 10 . 29 = 19 . 91 CL = c = 10 . 29 LCL = c − 3 c = 10 . 29 − 3 10 . 29 = 0 . 67 . The c chart is displayed in Figure 37.8. The process appears to be in statistical control.

20

UCL = 19.92

Sample count

15

c– = 10.29

10

5

LCL = 0.67

0 2

Figure 37.8

4

6

8

10

12 14 Day

16

18

20

22

24

c chart for number of billing errors.

The u Chart. The c chart monitors the number of nonconformities. The u chart, on the other hand, monitors the average number of nonconformities. As with the p and np charts, the resulting c and u charts for constant sample size will provide identical results. It is not necessary for the sample size to be constant from subgroup to subgroup for the u chart. Let ui be the average number of nonconformities for the ith subgroup (i = 1, 2, . . . , m), where ui =

Xi . n

Also, let u– represent the overall average number of nonconformities per unit, that is, m

u=

∑u i=1

m

i

.



The control limits and centerline for average number of nonconformities are u n

Part VI.F.5

UCL = u + 3 CL = u LCL = u − 3

u . n

The control limits, centerline, and ui are plotted on the control chart against the subgroup.

EXAMPLE 37.7 Reconsider the errors on billing statements example discussed previously. The average number of nonconformities ui for each day is given in Table 37.8. The overall average number of nonconformities per unit is then m

∑u

i

u=

i=1

m

=

0 . 20 + 0 . 90 + ... + 0 . 50 = 0 . 515 . 24

The control limits and centerline for the u chart are 0 . 515 u = 0 . 515 + 3 = 0 . 996 20 n CL = u = 0 . 515

UCL = u + 3

LCL = u − 3

Table 37.8

u 0 . 515 = 0 . 515 − 3 = 0 . 033 . n 20

Billing statement errors for a 24-day period.

Day

Number of errors

ui

Day

Number of errors

ui

1 2 3 4 5 6 7 8 9 10 11 12

4 18 14 7 7 8 16 6 10 12 9 8

0.20 0.90 0.70 0.35 0.35 0.40 0.80 0.30 0.50 0.60 0.45 0.40

13 14 15 16 17 18 19 20 21 22 23 24

10 13 3 12 17 13 9 17 9 9 6 10

0.50 0.65 0.15 0.60 0.85 0.65 0.45 0.85 0.45 0.45 0.30 0.50

Continued



The u control chart is displayed in Figure 37.9. Again, the process does not appear to be out of statistical control.

1.0

Sample count per unit

Part VI.F.5

Continued

UCL = 0.996

0.8

0.6 u– = 0.515 0.4

0.2 LCL = 0.033

0.0 2

Figure 37.9

4

6

8

10

12 14 Day

16

18

20

22

24

u chart for billing statement errors.

Cumulative Sum Control Charts So far, Shewhart control charts for monitoring various processes have been presented. Shewhart control charts are known to be poor at detecting small shifts in the process mean because they are based only on the current observation (see Gan [1991], Hawkins [1981, 1993], Lucas [1976], Montgomery [2008b], and Woodall and Adams [1993]). An alternative to the use of Shewhart control charts is the cumulative sum (CUSUM) control chart. The CUSUM control chart has been shown to be more sensitive to small shifts in the process because it is based on the current observation and the most recent past observations. Page (1961) first introduced the cumulative sum control chart. The control chart plots the cumulative sums of deviations of the observations from a target value. That is, the CUSUM control chart plots the quantity i

(

Ci = ∑ x j − m 0 j=1

)

against the subgroup i where



j

m0 is the target value for the process average As long as the process average remains at the target value m0, then the cumulative sums Ci will be approximately zero. If the process shifts away from the target mean, then Ci will increase in absolute value. Since the CUSUM chart uses information from the current and recent past observations, it can detect small shifts in the process more quickly than a standard Shewhart chart. CUSUM control charts can be used for subgroup data or individuals data. In addition, there have been applications for both variables and attributes data. The two-sided tabular CUSUM control chart for individuals will be presented here. One-sided CUSUM charts can be constructed if the interest is in a particular direction, downward or upward, but not necessarily both.

Part VI.F.5

Ci is the cumulative sum of deviations up to and including the ith sample x– is the mean of the jth sample

Tabular Form of the CUSUM. The tabular form of the two-sided CUSUM chart involves two statistics, Ci+ and Ci–. Ci+ represents the cumulative sum of deviations above the target mean and is referred to as the one-sided upper CUSUM. Ci– is the cumulative sum of deviations below the target mean and is referred to as the onesided lower CUSUM. Ci+ and Ci– are calculated as Ci+ = max ⎡⎣ 0, x1 − ( m 0 + K ) + C0+ ⎤⎦ Ci− = min ⎡⎣ 0, xi − ( m 0 − K ) + C0− ⎤⎦ where xi is the ith observation. Ci+ and Ci– are initially set at C0+ and C0 –. The constant K is a reference value and calculated as K=

m1 − m 0 2

where m0 is the target mean and m1 is the out-of-control mean that we are interested in detecting. If m1 is unknown, we can let K = ks where s is the process standard deviation and k is some constant chosen so that a particular shift is detected. To illustrate, suppose a shift from target of 1.5 standard deviations is important to detect. That is, we want to detect a shift from m0 to m0 – 1.5s or to m0 + 1.5s. In this case, K = 1.5s. If the process standard deviation is not known, it must be estimated from the data provided. The values of Ci+ and Ci– for each sample are plotted on a two-sided CUSUM control chart. If either value plots outside a stated decision interval (–H, H), the process is considered out of control. H should be chosen after careful consideration. There are many possible values for H, but a common setting is H = 5s. It has been shown that this decision value results in a low false alarm rate for the process under study. For further discussion of the design of CUSUM control charts see Hawkins (1993) or Woodall and Adams (1993).



Part VI.F.5

EXAMPLE 37.8 Packages of a particular instant dry food are filled by a machine and weighed. The weights (in ounces) for 24 successive packages have been collected and are displayed in Table 37.9. The target mean weight is m0 = 20 ounces. From past experience, it is believed that the process standard deviation is s = 0.20 ounces. If the process mean shifts from this target by one-half of the process standard deviation, then the filling process is deemed out of control. The engineer would like to design a two-sided CUSUM control chart and determine whether the process is indeed in control at the target m0 = 20 ounces. Some of the known or assumed quantities are: m0 = 20 ounces s = 0.20 ounces K = 0.5s = 0.5(0.20) = 0.10 H = 5s = 5(0.20) = 1 The CUSUM values Ci+ and Ci – are compared to the decision interval (–H, H) = (–1, 1). If any cumulative sum falls outside the interval (–1, 1), the process is considered to be out of control. To illustrate these calculations, consider the first observation, x1 = 20.26 ounces. Initially, C 0+ = C 0– = 0, and as previously shown, K = 0.1. The first cumulative sums are C 1+ = max ⎡⎣ 0 , x 1 − ( m 0 + K ) + C 0+ ⎤⎦ = max [ 0 , 20 . 26 − ( 20 + 0 . 1) + 0 ] = max [ 0 , 0 . 16 ] = 0 . 16 C 1− = min ⎡⎣ 0 , x i − ( m 0 − K ) + C 0− ⎤⎦ = min[ 0 , 20 . 26 − ( 20 − 0 . 1) + 0 ] = min[ 0 , − 0 . 36 ] = 0. Table 37.9

Weights for dry food packages.

Package

Weight (x i )

Package

Weight (x i )

1 2 3 4 5 6 7 8 9 10 11 12

20.26 19.97 19.76 19.72 19.69 19.85 19.96 20.03 20.06 19.71 19.68 19.94

13 14 15 16 17 18 19 20 21 22 23 24

20.30 19.77 20.40 19.98 19.91 20.18 20.08 20.05 20.20 19.90 19.95 20.12 Continued



Continued

Cumulative sum

1.0

Part VI.F.5

The remaining CUSUMs can be calculated similarly, but it is recommended that the calculations be done using a spreadsheet package or modern statistical package. Notice that both cumulative sums are within the decision interval (–1, 1), so the process has not signaled out of control at this point. The CUSUM chart is shown in Figure 37.10. The CUSUMs plot well within the decision interval, so there does not appear to have been a shift of 0.5s from the target value of 20 ounces.

UCL = 1

0.5

0.0

0

–0.5

–1.0

LCL = –1 2

Figure 37.10

4

6

8

10

12 14 Sample

16

18

20

22

24

CUSUM chart for package weight.

V-Mask Approach. The previous example was an illustration of the tabular CUSUM. A second method is the use of a V-mask, presented by Barnard (1959). The V-mask for the package weight example is given in Figure 37.11. A cumulative sum is calculated for each sample and plotted on the chart. The V-mask is constructed in such a way that if the process has not shifted out of control, all CUSUMs should plot within the V. Figure 37.11 indicates that the packagefilling process does not appear to have shifted out of control. This approach can be more difficult to implement although some modern statistical packages will construct the V-mask for particular problems. For more details on the V-mask approach and its drawbacks see Montgomery (2009b).



3

Cumulative sum

Part VI.F.5

2 1 Target = 0

0 –1 –2 –3 –4 2

Figure 37.11

4

6

8

10

12 14 Sample

16

18

20

22

24

V-mask for package weight.

Exponentially Weighted Moving Average Control Charts The exponentially weighted moving average (EWMA) control chart, like the CUSUM, is a good chart for detecting small shifts in the process mean parameter. The EWMA control chart was first introduced by Roberts (1959). The EWMA statistic is defined as zi = lxi + (1 – l)zi–1 where l is a weight and 0 < l ≤ 1, xi is the current observation, and zi–1 is the previous EWMA statistic. Initially, z0 = m0, the process target mean. If the process target mean is not known, then x– is used as the initial value. Like the CUSUM, the EWMA includes information from recent past observations as well as the current observation xi. Control limits can be placed on the values of zi. If one or more of the zi values fall outside the control limits, then the process is considered to be out of statistical control. The control limits for the EWMA for large values of i are ⎛ l ⎞ UCL = m 0 + Ls ⎜ ⎝ 2 − l ⎟⎠ ⎛ l ⎞ LCL = m 0 − Ls ⎜ ⎝ 2 − l ⎟⎠ where L is the width of the control limits. The values of L and l can significantly impact the performance of the chart. Small values of l work well in practice (0.05 ≤ l ≤ 0.25) with values of L between 2.6 ≤ L ≤ 3 (see Crowder [1989], Lucas and Saccucci [1990], and Montgomery [2009b]).



EXAMPLE 37.9

z i = l x i + ( 1 − l )z i − 1

Part VI.F.5

Reconsider the 24 packages being filled by a machine and weighed in the previous example. The package weights are displayed in Table 37.9. The target mean weight is m0 = 20 ounces. From past experience, it is believed that the process standard deviation is s = 0.20 ounces. We would like to construct an EWMA control chart for these data using l = 0.10 and L = 2.7. The EWMA statistic is = 0 . 10 x i + ( 1 − 0 . 10 )z i − 1 = 0 . 10 x i + 0 . 90 z i − 1. To illustrate the calculation of the EWMA statistic for each observation, consider the first observation, x1 = 20.26 ounces. If we initialize the process using z0 = m0 = 20, z1 is found to be z 1 = 0 . 10 x i + 0 . 90 z 0 = 0 . 10( 20 . 26 ) + 0 . 90( 20 ) = 20 . 02 6 . The remaining EWMA statistics are calculated similarly. The control limits and centerline are ⎛ l ⎞ ⎛ 0 . 10 ⎞ UCL = m 0 + L s ⎜ = 20 + 2 . 7( 0 . 2) ⎜ = 20 . 12 ⎝ 2 − 0 . 10 ⎠⎟ ⎝ 2 − l ⎟⎠ CL = 20 ⎛ l ⎞ ⎛ 0 . 10 ⎞ LCL = m 0 − L s ⎜ = 20 − 2 . 7( 0 . 2) ⎜ = 19 . 88 . ⎝ 2 − 0 . 10 ⎟⎠ ⎝ 2 − l ⎟⎠ The control limits and the EWMA statistics zi are plotted on the EWMA control chart in Figure 37.12. The process appears to be in control since all EWMA statistics fall within the control limits. 20.15 UCL = 20.12 20.10

zi

20.05

CL = 10.29

20.00

19.95

19.90 LCL = 19.88 0

5

10

15

20

25

Sample

Figure 37.12

EWMA control chart for package weight.



Part VI.F.5

Patterns and Trends on the CUSUM and EWMA Patterns and trends on the CUSUM and EWMA control charts do not necessarily indicate an out-of-control process. The statistics plotted on the charts are correlated since they are functions of not only the current observation but also recent past observations. As such, patterns can be expected even with an in-control process.

Choosing a Control Chart The choice of which control chart to use in a particular problem depends on the process under investigation. Shewhart control charts are easy to construct and interpret but they are less effective at detecting small shifts in the process parameter. It has also been shown that Shewhart control charts are very sensitive to the assumption of normality (see Borror, Montgomery, and Runger [1999]). That is, if the underlying distribution of the process is nonnormal, then Shewhart charts can often signal out-of-control when in fact the process is in control. CUSUM and EWMA control charts are quite robust to departures from normality and are better at detecting small shifts in the process than the Shewhart charts.

Moving Average Control Chart The moving average (MA) control chart is similar to the EWMA in that it uses a moving average of a certain span (not necessarily consecutive observations). However, the moving average is an unweighted average of the observations. Suppose there are n observations, x1, x2, . . ., xn selected from the process with mean m0 and standard deviation s. Furthermore, suppose a moving average of span w is of interest. The moving average statistic at time i can be written as MAi =

xi + xi−1 + ... + xi − w +1 w

The values of MAi are plotted on a control chart with control limits and centerline: UCL = m 0 +

3s w

CL = m 0 LCL = m 0 −

3s w

The moving average chart may be suitable for the following situations: • When data are collected periodically or it may take some time to produce a single item • When it may be desirable to dampen the effects of overcontrol • When it may be necessary to detect smaller shifts in the process than with a comparable Shewhart chart



6. CONTROL CHART ANALYSIS

Body of Knowledge VI.F.6

Part VI.F.6

Read and interpret control charts, use rules for determining statistical control. (Evaluate)

A critical tool in the analysis of charted data is the process log. The process log may be a separate document or it may be maintained as part of the control chart itself. Entries in the log should include all changes in the process and its environment, including maintenance, raw materials, adjustments, tooling, fixturing, and so on. Each of the control limit formulas discussed in the previous section uses data from the process. Although it is not always obvious from the formulas, the upper and lower limits are placed at ±3s from the average. The use of three-sigma limits is a direct result of the underlying assumption of normality. It can be shown that if the underlying distribution is normal, then approximately 99.7 percent of all the data will lie within three standard deviations of the mean. Therefore, if an observation falls beyond three standard deviations from the mean, that observation would be flagged as unusual since the probability of this occurring is 0.003 and may be an indication of an out-of-control process. Since the Shewhart control charts are based on the normality assumption, it is common to use three standard deviations in the construction of the control limits for these charts. For the EWMA control chart, if L =3 and l = 1, then the control limits would reduce to the standard Shewhart control limits. But it has been shown that values other than L = 3 and l = 1 can result in well-performing control charts, especially for detecting small shifts in the process parameter. It should be noted that the probability of a point falling inside or outside three standard deviations is somewhat theoretical because no process runs as if its output were randomly selected numbers from some historical distribution. It is enough to say that when a point falls outside the control limits, the probability is quite high that the process has changed. When the probability is very high that a point did not come from the distribution used to calculate the control limits, the process is said to be out of statistical control. Unfortunately, this is often abbreviated to “out of control,” which seems to imply some wild action on the part of the process. In reality, the out-of-statistical-control condition is often very subtle and would perhaps not be detected without the control chart. This, in fact, is one of the main values of the control chart: it detects changes in a process that would not otherwise be noticed. This may permit adjustment or other action on the process before serious damage is done. One of the hazards of using a control chart without proper training is the tendency to react to a point that is not right on target by adjusting the process, even though the chart does not indicate that the process has changed. If an adjustment is made whenever a point is not exactly on target, it may tend to destabilize a stable process. In the ideal situation, a process should not need adjustment except



Part VI.F.6


when the chart indicates it is out of statistical control. Dr. W. E. Deming states that “The function of a control chart is to minimize the net economic loss from . . . overadjustment and underadjustment.” (Deming 1986)

Rules for Determining Statistical Control A number of events are very unlikely to occur unless the process has changed, and thus serve as statistical indicators of process change. The lists of rules that reflect these statistical indicators vary somewhat from textbook to textbook, but two of the most widely used lists of rules are the eight rules used by the software package Minitab and the six rules listed by the Automotive Industry Action Group (AIAG) in its SPC manual. The eight Minitab rules are: 1. One point more than 3s from the centerline (either side) 2. Nine points in a row on the same side of the centerline 3. Six points in a row, all increasing or all decreasing 4. Fourteen points in a row, alternating up and down 5. Two out of three points more than 2s from the centerline (same side) 6. Four out of five points more than 1s from the centerline (same side) 7. Fifteen points in a row within 1s of the centerline (either side) 8. Eight points in a row more than 1s from the centerline (either side) The AIAG in the third edition of its SPC manual lists a “Summary of Typical Special Cause Criteria” that is identical to Minitab’s list except for rule 2, which says: 2. Seven points in a row on one side of the centerline The AIAG SPC manual emphasizes that “. . . the decision as to which criteria to use depends on the process being studied/controlled.” CQEs may find it useful to generate additional tests for particular situations. If, for instance, an increase in values represents a safety hazard, it would not be necessary to wait for the specified number of successively increasing points to take action. The ±3s location for the control limits is somewhat arbitrary and could conceivably be adjusted based on the economic trade-off between the costs of not taking action when an out-ofcontrol condition occurs and taking action when an out-of-control condition has not occurred. Deming has stated, however, that moving the control limits up and down can be a source of additional problems, and it would be better in most cases to put that energy into reducing variation. Sensitizing rules should always be used with caution. Although sensitizing rules can improve a Shewhart chart’s ability to detect small shifts, they can seriously degrade the performance of the chart when the process is indeed in



Part VI.F.6

control. Control chart performance is often measured by the average run length (ARL), which is defined as the number of cycles, time periods, or samples that elapse before the process signals out-of-control. If the process is in control, we want the average run length to be large. If the process is out of control, a small average run length is desirable. When several sensitizing rules are used simultaneously on a control chart, the in-control average run length can become unacceptably small. For example, suppose that independent process data are being monitored using a standard Shewhart control chart. For an in-control process, the average run length is approximately 370. However, the Shewhart control chart with Western Electric rules (Western Electric [1956]) has an in-control average run length of approximately 91 (see Champ and Woodall [1987]). That is, even if the process is in statistical control, the sensitizing rules may lead to more false alarms than the standard Shewhart control chart with no sensitizing rules. The important issue, of course, is not the exact wording of the rules so much as the action that takes place once the unusual event has occurred. The first step always should be to ascertain that the point is calculated and plotted correctly. If possible, a double check should be made on the measurement itself. For variables charts, the range section should be analyzed first. Increases in the range values represent increased variation between the readings within an individual sample. Possible causes include bearings, tooling, or fixtures. In the case of cutoff operations, for instance, if the part is pushed against a backstop for measurement, the backstop could have become “rubbery.” Changes in the averages chart represent some sort of shift in the process. Frequent causes are tool wear, changes in raw materials, and changes in measurement systems or process parameters such as machine settings, voltages, pneumatic pressure, and so on. It is useful to construct a list of things to check when certain chart characteristics occur. Such a list can come from a discussion among experienced personnel as well as from data from a process log. In some cases the events on the “out of control” lists represent improved situations. For instance, the process is considered out of control if too many points are in the middle third of the control limit area. Recall that the control chart tests are used to help determine whether the current values come from the distribution that was used to calculate the control limits. If too many points are grouped around the centerline, the points probably come from a different distribution. The process should be investigated to determine what changed and to see whether this change can be perpetuated. If a log is maintained for the process, it may be possible to find changes that correspond to the time that the improvement occurred. Experience is the best teacher when it comes to chart interpretation. Efforts should be made to document a body of knowledge about each process. Finally, note that a control chart is really a graphical hypothesis test. The null hypothesis is that the process has not changed, and as each point is plotted, the chart is examined to determine whether there is sufficient evidence to reject the null hypothesis and conclude that the process has changed. The significance level varies somewhat with the chart test employed.



Part VI.F.7

7. PRE-CONTROL CHARTS

Define and describe how these charts differ from other control charts and how they should be used. (Apply) Body of Knowledge VI.F.7

A pre-control chart is sometimes used in place of a control chart or until sufficient data are collected to construct a control chart. An important difference between pre-control charts and control charts is that the upper and lower pre-control limits are calculated from the tolerance limits rather than from data from the process. Thus the pre-control chart is not statistical in the sense that the distribution of the current process is not being compared to some historic distribution. A fairly standard way to construct the pre-control (PC) limits is to multiply the value of the tolerance (upper specification limit – lower specification limit) by 0.25. Then subtract the resulting value from the upper specification limit to form the upper PC limit and add it to the lower specification limit to form the lower PC limit. As parts are measured, their values are compared to the PC limits and appropriate action is taken based on rules such as these: 1. If the first part is outside the specification limits, adjust the process. 2. If a part is inside the specification limits but outside PC limits, measure the next part. 3. If two successive parts are outside PC limits, adjust the process. 4. If five successive parts are inside PC limits, consider switching to less frequent measuring. There has been much debate about the use of pre-control charts. Some of the advantages and disadvantages of precontrol charts follow. Advantages • Pre-control charts are easy to implement and interpret. • Pre-control charts can be very useful in initial setup operations in determining whether product being produced is centered between the tolerances. Disadvantages • Pre-control does not provide information about how variability can be reduced if necessary or how the process can be brought back into control.



Part VI.F.7

• Pre-control charts should only be used for processes whose process capability ratio (to be discussed in the next section) is greater than one. If the capability of the process is very poor, then pre-control charts will signal that the process should be stopped and assignable causes found. But low capability does not necessarily indicate that any assignable causes are actually present. That is, unnecessary tampering will most likely occur in this case. • The small sample sizes used in pre-control will greatly reduce the chart’s ability to detect moderate to large shifts.

EXAMPLE 37.10 The specification for a dimension is 5.000 ± 0.010. The tolerance is 0.020, so 25 percent of the tolerance is 0.005. Therefore, the upper PC limit would be placed at 5.005 and the lower PC limit at 4.995, as indicated in Figure 37.13. The actions to be taken at each of the lettered points in Figure 37.13 are: A. Adjust process B. Measure another part C. Measure another part D. Measure another part

5.010

Upper specification limit

5.005

Upper PC limit

5.000

4.995

Lower PC limit

4.990

Lower specification limit

A

Figure 37.13

B

C

D

E

F

G

H

Example of a pre-control chart. Continued



Part VI.F.8

Continued

E. Adjust process F. Adjust process G. Measure another part H. Measure another part Various authors provide additional rules. The principal advantage of pre-control is that it is simpler than standard control charts. The main disadvantage is that it is not statistically based. When the pre-control rules indicate that the process should be adjusted, there is not necessarily a high probability that the process has changed. This may lead to overadjustment and decreased stability of the process. For this reason, there is some controversy over the use of pre-control, with Montgomery (2009b) stating that “This author believes that pre-control is a poor substitute for standard control charts and would never recommend it in practice.”

The material presented here can be found in greater detail in Ledolter and Burrill (1999) and in an article by Ledolter and Swersey (1997).

8. SHORT-RUN STATISTICAL PROCESS CONTROL

Identify, define, and use short-run SPC rules. (Apply) Body of Knowledge VI.F.8

The control charts presented to this point apply to processes that are considered long, continuous production runs. These charts are not appropriate for short production runs. Short production runs are commonplace and include processes that produce built-to-order product or quick turnaround production. Short-run control charts should be considered when data are collected infrequently or aperiodically. They may be used with historical target or target values, attribute or variable data, and individual or subgrouped averages. Standardized control charts are commonly used to monitor short production runs. A simple illustration for attribute data will be presented. For complete details on short production runs, see Montgomery (2009b).

Short-Run SPC for Attribute Data The short-run control charts for attribute data are actually standardized control charts. The attribute for the control chart of interest is standardized and plotted on a control chart. To illustrate, consider the standardized value using the number of nonconformities (that is, c chart). The standardized value is



Zi =

ci − c c

,

• Each data point is standardized. • The standardized random variable Zi has a standard normal distribution.

Part VI.F.8

which follows a standard normal distribution. The following properties of all standardized control charts will apply:

• The centerline for all standardized charts is zero. • The control limits for all of the standardized charts are –3 and 3.

EXAMPLE 37.11 Nonconformities are counted on 10 printed circuit boards. The boards come from a short production run. The nonconformities are given in Table 37.10. A short-run c chart is appropriate for this situation. To construct the short-run control chart on the number of nonconformities, we first calculate the average number of nonconformities c– , then calculate the standardized values Zi =

ci − c . c

For this problem, c– = 25/10 = 2.5 (where there are a total of 25 nonconformities and 10 boards). The standardized values are then found using Zi =

ci − 2 . 5 . 2.5

To illustrate, the standardized value for the first circuit board is Z1 =

Table 37.10

c1 − 2 . 5 4 − 2 . 5 = = 0 . 95 . 2.5 2.5

Number of nonconformities for printed circuit boards.

Printed circuit board

Number of nonconformities

1 2 3 4 5 6 7 8 9 10

4 0 1 3 6 3 1 0 5 2

Total

25 Continued



Part VI.F.8

Continued

The remaining standardized values are calculated similarly. The short-run c chart is shown in Figure 37.14. The process does not appear to be out of statistical control.

3

3

2

z

1 0

0

–1 –2 –3

–3 1

Figure 37.14

2

3

4

5 6 Board

7

8

9

10

Short-run c chart for printed circuit boards.

There are a number of methods for constructing control charts for short production runs. The EWMA and CUSUM control charts can be very effective in this situation. See Hawkins and Olwell (1998) for more details on the CUSUM approach for short production runs.



Chapter 38 Part VI.G.1

G. Process and Performance Capability

T

his chapter addresses four aspects of process and performance capability: process capability studies, process performance versus specifications, process capability indices, and process performance indices.

1. PROCESS CAPABILITY STUDIES Define, describe, calculate, and use process capability studies, including identifying characteristics, specifications, and tolerances, developing sampling plans for such studies, establishing control, etc. (Analyze) Body of Knowledge VI.G.1

The purpose of a capability study is to determine whether a process is capable of meeting certain requirements. Capability of a process can be evaluated through determination of a probability distribution, its shape, center, and spread. Tools such as histograms, probability plots, and stem-and-leaf plots can be used to evaluate process capability without having stated specification limits for the quality characteristic of interest. Process capability is often investigated with respect to given specifications. In theory, a capability study should be performed for every product dimension and every quality characteristic. In practice, however, people familiar with a process usually are able to identify the few characteristics that merit a full capability study, usually the characteristics that experience has shown to be difficult to hold to specification. For example, suppose a customer requires certain process outputs to be 45 to 55, such as: • The arrival time for a delivery vehicle must be between 45 and 55 minutes after the hour. • Manufactured pumps must produce between 45 psi and 55 psi. • The plating thickness must be from 45 to 55 mm. 527



Part VI.G.1


In these instances, the 45 to 55 requirement is called the specification, which typically is inclusive of the endpoints. Bothe (1999) identifies six major activities as parts of a process capability study: 1. Verifying process stability 2. Estimating process parameters 3. Measuring process capability 4. Comparing actual capability to desired capability 5. Making a decision concerning process changes 6. Reporting the results of the study with recommendations These six areas are not unique and may require several different methods to complete any one activity. For example, control charts and designed experiments can be implemented to estimate process capability. In addition, Montgomery (2009b) recommends the use of histograms and probability plotting in addition to process capability ratios (presented in this section) as techniques useful in determining the capability of a process. The first step in conducting a capability study is to verify that the process is stable. A stable process can be thought of as a process without special causes of variation present. Process stability can be determined by using a control chart. The process is considered to be stable if the chart shows that no special causes are present after an appropriate number of points have been plotted. A key phrase in the previous sentence is “appropriate number of points.” Although authorities disagree on the number of points needed, 20 to 30 points are commonly used. However, the more points you plot, the higher the confidence you can have in the stability conclusion. The second step in conducting a capability study is to determine whether it is reasonable to assume that the process data come from a normal distribution. To do this, a normal probability plot or histogram could be constructed using the original readings (not the averages) from the control chart. If the histogram looks normal, with most points grouped around a single peak and fairly symmetric tails on each side, you may assume that the data constitutes a sample drawn from an approximately normal population. Using a normal probability plot, we conclude that the normality assumption is satisfied if the data fall along a straight line. Again, the more data you use, the greater the confidence you can have in this conclusion. The normality assumption is absolutely necessary in order for the results of a process capability study (process capability ratios, discussed next) to be considered valid. If the data are nonnormal, a transformation to induce normality may be necessary. Kotz and Lovelace (1998) also discuss process capability indices that can be used for nonnormal distributions. For information about a hypothesis test for normality, refer to Devore (2007) or Montgomery and Runger (2006). If the data are normally distributed, the next step is to use the normal distribution to estimate process capability. The most common method is to use the data from a control chart to estimate m and s. The sampling plan is then the same as that used for the control chart. Once the chart exhibits statistical control, the values of x– and R calculated from the control chart are used in the capability analysis formulas.


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2. CALCULATING PROCESS PERFORMANCE VERSUS SPECIFICATIONS Part VI.G.2

Distinguish between natural process limits and specification limits, and calculate percent defective. (Analyze) Body of Knowledge VI.G.2

In this section the capability of a process in relation to specification limits will be investigated. The capability of a process could be described by the fraction of units that fall outside the specification limits. To illustrate, consider the following example.

EXAMPLE 38.1 A dimension has specifications of 2.125 ± 0.05. Data from the process indicate that the distribution is normally distributed, and the x– and R control chart indicates that the process is stable. The control chart used a sample of size five and it is found that x– = 2.1261 – and R = 0.0055. We wish to determine what fraction of the manufactured product will have this particular dimension outside the specification limits.

Solution: Let X represent the dimension of the quality characteristic of interest. What we are looking for is the fraction of the manufactured product that will have this particular dimension outside the specification limits; this can be written as P(2.120 < X < 2.130). Since X is normally distributed, we can use the standard normal distribution to determine this fraction. The best point estimate for m is x– = 2.1261. The point estimate for process standard deviation s is given by the formula sˆ =

R 0 . 0055 = = 0 . 00236 . 2 . 326 d2

The constant d2 can be found in Appendix C for n = 5. The estimated fraction that does conform to specifications is ⎛ 2 . 120 − 2 . 1261 X − x 2 . 130 − 2 . 1261⎞ P ( 2 . 120 < X < 2 . 130 ) = P ⎜ < < ⎝ 0 . 00236 sˆ 0 . 00236 ⎟⎠ = P ( − 2 . 58 < Z < 1 . 65) = P ( Z < 1 . 65) − P ( Z < − 2 . 58 ) = 0 . 9505 − 0 . 0049 = 0 . 9456 . Therefore, the fraction that is nonconforming is 1 – 0.9456 = 0.0544. Approximately 5.44 percent of the products will fall outside specification for this quality characteristic.



Control Limits, Specification Limits, and Natural Tolerance Limits

Part VI.G.3

It should be noted that there is a significant difference between control limits, specification limits, and natural tolerance limits. Control limits are determined by the natural tolerance of the process while specification limits are determined externally—usually by management, engineers, customers, and so on. There is no relationship between specification limits and control limits. Suppose we are monitoring the sample mean x– where the population of interest is normally distributed with mean m and standard deviation s. The different limits could be written as: Natural limits: m ± 3s Control limits: x ± 3

s n

Specification limits: [LSL , USL] (determined externally)

3. PROCESS CAPABILITY INDICES

Define, select, and calculate Cp, Cpk, Cpm, and Cr, and evaluate process capability. (Evaluate) Body of Knowledge VI.G.3

Various capability indices have been developed to try to quantify process capability in a single number. The stability and normality requirements discussed earlier must be met for these measures to be effective. Four such indices are Cp, Cr, Cpk, and Cpm.

Cp Cp compares the tolerance (the width of the engineering specifications) with the natural process tolerance. Cp is given by Cp =

USL − LSL 6s

where LSL is the lower specification limit and USL is the upper specification limit. The true process standard deviation s is usually unknown and must be estimated – from the sample data. We can use the sample standard deviation s or sˆ = R/d2 if control charts are used in the analysis. The estimate of Cp is ˆ = USL − LSL . C p 6sˆ



Consider the previous example involving a quality characteristic with specification limits set at 2.125 ± 0.05 and sˆ = 0.00236. Our estimate of Cp is

Generally, it is desirable for Cp > 1. Based on this analysis, the process does not appear to be capable.

Part VI.G.3

ˆ = USL − LSL = 2 . 130 − 2 . 120 = 0 . 706 . C p 6sˆ 6(0 . 00236)

Cr The Cr measure is simply the inverse of Cp, that is, Cr = 1/Cp. An estimate of Cr is ˆ = C r

6sˆ . USL − LSL

A simple interpretation of Cr is the percentage of the tolerance (or specification band) that is used up by the process. Consider our example again where it was found that Cˆ p = 0.706. Then Cˆ r = 1/0.706 = 1.416. This ratio is sometimes referred to as the capability ratio, and smaller values are better.

Cpk The capability measure Cpk penalizes a process whose mean is off center. Cpk takes into account process centering and is given by ⎡ USL − m m − LSL ⎤ C pk = min ⎢ , . 3s ⎥⎦ ⎣ 3s It is desirable to have a value of Cpk > 1, which indicates that the process exceeds the stated minimum requirement. An estimate of Cpk is ˆ = min ⎡ USL − x , x − LSL ⎤ . C pk ⎢ 3sˆ 3sˆ ⎥⎦ ⎣ For our example, ˆ = min ⎡ USL − x , x − LSL ⎤ C pk ⎢ 3sˆ 3sˆ ⎥⎦ ⎣ ⎡ 2 . 130 − 2 . 1 261 2 . 1261 − 2 . 120 ⎤ = min ⎢ , 3(0 . 00236) ⎥⎦ ⎣ 3(0 . 00236) = min ⎡⎣ 0 . 551, 0 . 862 ⎤⎦ = 0 . 551 . Historically, a Cpk value of 1.0 or larger was considered capable. This would be equivalent to stating that the natural process limits lie inside the tolerance limits.



Part VI.G.4

More recently, quality requirements have become more stringent, and many customers require Cpk values of 1.33, 1.66, or 2.00.

Cpm The previous measure Cpk was developed to take into account centering of the process. However, studies have shown that a large value of Cpk does not necessarily indicate that the location of the process mean is centered between the LSL and USL. A measure was developed to provide a better measure of centering. This measure is denoted by Cpm and given by USL − LSL

C pm =

2 6 ⎡⎢s 2 + ( m − T ) ⎤⎥ ⎣ ⎦

.

An estimate is then ˆ = C pm

USL − LSL 2 ⎡ 6 ⎢sˆ 2 + ( x − T ) ⎤⎥ ⎣ ⎦

where T is the process target. Again, the estimates of m and s are obtained from control charts. The point estimators for the capability indices given in this section each have some degree of error or variability associated with them. It has been recommended that confidence intervals on the process capability indices be constructed to quantify the precision associated with the point estimators. The reader is encouraged to see Kotz and Lovelace (1998) for complete details of the point estimators for and confidence intervals on these and other capability indices.

4. PROCESS PERFORMANCE INDICES Define, select, and calculate Pp and Ppk and evaluate process performance. (Evaluate) Body of Knowledge VI.G.4

Performance indices provide a picture of current process operation and have been used for comparison and prioritization of improvement efforts. Two such performance indices are Pp and Ppk. The performance indices have been recommended for use when the process is not in statistical control. The formulas for Pp and Ppk are equivalent to those for Cp and Cpk, respectively, except that the sample standard deviation



n

i=1

2

i

n−1

is used instead of s. The Automotive Industry Action Group (AIAG) and the American National Standards Institute (ANSI) recommend the use of Pp and Ppk when the process is not in control. This is a somewhat controversial position because an out-of-control process is by definition unpredictable. Montgomery (2009b) states, “The process performance indices Pp and Ppk are actually more than a step backwards. They are a waste of engineering and management effort—they tell you nothing.” Wheeler (2005) disagrees with Montgomery and uses Pp and Ppk to calculate what he refers to as the effective cost of production. The reader is encouraged to see Kotz and Lovelace (1998) for more discussion on performance and capability indices.

Part VI.G.4

s=

∑ (x − x )

Short-Term versus Long-Term Process Capability In general, the longer the time span over which the data are collected, the more valid the capability analysis. The analysis of data collected over a few hours can provide information about the process during those hours and may be useful for comparison purposes during process improvement efforts. Using control charts for process capability allows for the evaluation of both short-term process capability and long-term process capability. For example, x– and R charts provide both instantaneous variability and variability over time.

Nonnormal Data Transformations (Process Capability for Nonnormal Data) Once again, verifying the normality of a process is important. If the underlying distribution is not normal, the indices described in this chapter may not be valid. Various transformations and alternative indices have been proposed when the distribution is nonnormal. See Kotz and Lovelace (1998), Luceño (1996), Montgomery (2009b), and Rodriquez (1992) for details on dealing with nonnormality and process capability.



Part VI.H.1

Chapter 39 H. Design and Analysis of Experiments

E

xperiments are an essential part of research and process and product development. It is important to correctly design and implement any experiment to obtain statistically valid results. All experiments can be considered “designed” experiments, but some of them may be designed poorly. Positive results can be achieved when a statistically designed experiment is developed and implemented correctly. Some of the results of a good experimental design include: • Improvement in process yield • Reduction in process variability (closer conformance to nominal or target requirements is often achieved) • Reduction in design and development time • Reduction in operation costs Before discussing the actual design and implementation of valid experiments, some important terminology must be introduced.

1. TERMINOLOGY

Define terms such as dependent and independent variables, factors, levels, response, treatment, error, and replication. (Understand) Body of Knowledge VI.H.1

This section provides definitions for several important basic terms.

Dependent Variable (Response) In experimental design, the dependent variable or response is the result or outcome of interest of the experiment. For example, yield of a process, time to complete a task, taste score, and so on. 534


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Independent Variables (Factors)

• Type of gasoline (such as standard, plus, or super)

Part VI.H.1

In experimental design these variables, sometimes referred to as treatments or factors, are chosen by the experimenter or practitioner to determine what effect, if any, they will have on the outcome of the experiment. Examples:

• Condensation temperature and its effect on yield • Carbonation level in a test of a soft-drink taste • Supplier of raw material in a manufacturing process Factors can be quantitative (for example, temperature, amount of fertilizer per acre) or qualitative (for example, technician, different additives, supplier, or type of keyboard). There may be more than one factor under investigation in any one experiment. In addition, factors can take on one of a number of roles. For example, control factors are process inputs to be controlled in actual production. These factors can be adjusted in practice to affect the output of a process. Noise factors, on the other hand, can be controlled during the experiment, but are allowed to vary naturally in actual production. These factors are difficult to control in practice and can introduce variability into the response of interest. Understanding the effect of noise factors on the response can aid in reducing this variability in practice while not completely removing it. Examples of noise factors can include humidity within a manufacturing plant, ambient temperature, how a product is actually used in practice, and so on.

Levels Levels in experimental design refers to the levels of the factors. For example, temperature levels of 200, 300, and 400oC; cooking times of one hour or two hours; two suppliers, A and B; and percent additive of 0.2, 0.5, and 0.8 percent.

Treatment A treatment in experimental design refers to a combination of the levels of each factor assigned to an experimental unit. This is sometimes called a treatment combination. To illustrate, consider an experiment on the breaking strength of a material. Two factors of interest are the machine (M1, M2, M3) on which the material is produced and the technician (T1, T2) using the machine. One treatment combination would be technician 2 using machine 1 (T2M1). “Treatment” is a term left over from the early days of experimental design and its roots in agricultural experimentation.

Factorial Designs Factorial designs are those where all treatment combinations of the factors are carried out. Suppose an experiment involves three factors, A, B, and C, with three,



Part VI.H.1

five, and two levels investigated, respectively. A full-factorial design would consist of 3 × 5 × 2 = 30 treatment combinations.

Error Error in experimental design has several meanings. In any experimental situation, error could represent errors in experimentation, errors of measurement, variation in materials or factors in general, or the effect of noise factors on the response, for example. Experimental error is the variability that is observed when a treatment combination is repeated, that is, replicated.

Replication In experimental design, replication is the repetition of the basic experiment. This would involve a complete reset of the factor levels and repeating the experiment. Replication will provide an estimate of experimental error and leads to more precise estimates of the factor effects. It should be noted that multiple measurements of a treatment combination do not necessarily constitute replication. There is a significant difference between true replication and repeated measures.

Putting It All Together The objective of a designed experiment is to generate knowledge about a product or process. The experiment seeks to find the effect a set of independent variables has on a set of dependent variables. Mathematically this relationship can be denoted y = f(x) + e, where x is an independent variable and y is the dependent variable (although there will most likely be more than one independent variable). For example, suppose a machine operator who can adjust the feed, speed, and coolant temperature wishes to find the settings that will produce the best surface finish. The feed, speed, and coolant temperature are the independent variables or factors. Surface finish is the dependent variable or response and its value depends on the values of the independent variables. Independent variables may also be thought of as input variables, and dependent variables as output variables. There may be additional independent variables, such as the hardness of the material or humidity of the room, that have an effect on the dependent variable. Other factors, such as hardness or humidity, are considered noise factors since they may induce variability in the surface finish but can not necessarily be controlled in actual production. In this example, the experimental design may specify that the speed will be set at 1300 rev/min for part of the experiment and at 1800 rev/min for the remainder. These values are referred to as the levels of the speed factor. The team decides to test each factor at two levels, as follows: • Feed (F): 0.01 and 0.04 in/rev • Speed (S): 1300 and 1800 rev/min • Coolant temperature (C): 100° and 140° F A full-factorial design for the three factors will be used. A full-factorial experiment tests all possible combinations of levels and factors, using one run for each



Run #

Feed

Speed

C temp

1

0.01

1300

100

2

0.01

1300

140

3

0.01

1800

100

4

0.01

1800

140

5

0.04

1300

100

6

0.04

1300

140

7

0.04

1800

100

8

0.04

1800

140

Figure 39.1

1

2

3

4

5

Part VI.H.1

combination. The total number of combinations is given by LF where F represents the number of factors of interest each with L levels. In this situation, the number of treatment combinations is 23 = 8. The team develops a data collection sheet listing those eight experiments with room for recording five replicates (n = 5) for each run (see Figure 39.1). As the data are collected, the values are recorded as shown in Figure 39.2. These data are also referred to as the response values since they show how the process or product responds to various treatments. Note that the five values for a particular run are not all the same. This may be due to drift in the factor levels, variation in the measurement system, and/or the influence of noise factors. The variation observed in the readings for a particular run is referred to as experimental error. If the number of replications is decreased, the calculation of experimental error is less accurate although the experiment has a lower total cost. If all the factors that impact the dependent variable are included in the experiment and all measurements are exact, replication is not needed and a very efficient experiment could be conducted. Thus, the accurate determination of experimental error and cost are competing design properties.

A 2 3 full-factorial data collection sheet.

Run #

Feed

Speed

C temp

1

2

3

4

5

1

0.01

1300

100

10.1

10.0

10.2

9.8

9.9

2

0.01

1300

140

3.0

4.0

3.0

5.0

5.0

3

0.01

1800

100

6.5

7.0

5.3

5.0

6.2

4

0.01

1800

140

1.0

3.0

3.0

1.0

2.0

5

0.04

1300

100

5.0

7.0

9.0

8.0

6.0

6

0.04

1300

140

4.0

7.0

5.0

6.0

8.0

7

0.04

1800

100

5.8

6.0

6.1

6.2

5.9

8

0.04

1800

140

3.1

2.9

3.0

2.9

3.1

Figure 39.2 A 2 3 full-factorial data collection sheet with data entered.



Part VI.H.2


Run #

Feed

Speed

C temp

Average surface finish reading

1

0.01

1300

100

10

2

0.01

1300

140

4

3

0.01

1800

100

6

4

0.01

1800

140

2

5

0.04

1300

100

7

6

0.04

1300

140

6

7

0.04

1800

100

6

8

0.04

1800

140

3

Figure 39.3

A 2 3 full-factorial data collection sheet with run averages.

Once the data are collected as shown in Figure 39.2, it may be useful to find the average of the five replication responses for each run. These averages are shown in Figure 39.3.

2. PLANNING AND ORGANIZING EXPERIMENTS

Define, describe, and apply the basic elements of designed experiments, including determining the experiment objective, selecting factors, responses, and measurement methods, choosing the appropriate design, etc. (Analyze) Body of Knowledge VI.H.2

Planning and organizing a designed experiment is just as important as conducting the experiment and analyzing the results. Some of the important steps in planning and organizing experiments include 1. State the objective 2. Choose the factors and responses 3. Define measurement methods 4. Choose an appropriate design The team making these types of choices and decisions should include engineers, technicians, management, customers, statisticians, and others who have firsthand knowledge of and experience with the process under study. It is important to ensure that the experiment is conducted as planned. Errors that occur as the experiment is carried out or errors in the measurements could deliver invalid results.



Objective The objective of a designed experiment is considered the goal of the experiment. Recognition of and statement of the problem is the first step in designing a successful experiment. Although stating the objective of the problem may seem obvious, it is not always given due consideration in the initial stages of planning the experiment.

Part VI.H.2

When preparing to conduct an experiment, the first consideration is, “What question are we seeking to answer?” In the example illustrated in the previous section the objective was to find the combination of process settings that minimize the surface finish reading.

Choice of Factors The response (or responses) of interest are the outputs to be measured. The responses should represent all aspects of quality, productivity, and functionality. What factors might significantly affect the response? In most processes we could measure a very large number of variables of which only a few have any real impact on the response. Initially, many factors should be included and screening experiments carried out to eliminate those factors that do not significantly affect the response. One task in designing an experiment is to maximize the chance of including the significant variables in the design and leaving out those that have little impact. The levels of the factors should also be given serious consideration. The span or scope of the experimental conditions will have an impact on one’s ability to determine the significance of a factor. For example, should the range of temperature be from 100° C to 200° C or 125° C to 175° C for a particular problem? If the range is too narrow, important effects could be completely missed.

Responses and Measurement Methods Once the objective of the experiment has been determined and factors and levels selected, an appropriate measurement system is chosen. The measurement method is determined by the response that has been decided on. For example, if the outcome measured is placed into one of several possible categories (categorical data), the response that will be modeled or used in the analysis would be quite different than if the measured outcome is continuous. The measurement system must be appropriate for the type of response of interest and can only be determined by people familiar with the process and output. Regardless of the type of response, methods exist that can adequately address these issues. This is discussed in Section 3 of this chapter.

Choice of Design Once the objective of the experiment has been decided on, the factors, levels, and responses determined, and the method of measurement chosen, the next step is to choose the type of design to be used. The choice of design will depend on the



Part VI.H.3


previous steps (stating the objective, choosing factors, levels, and responses, and determining the measurement method). Other important considerations include the size of the design that is acceptable, the number of replicates, the run order of the design, and whether or not blocking is involved. Many standard statistical packages aid the practitioner in determining an appropriate design. In choosing the appropriate design, the objective of the experiment should always be kept in mind. Therefore, rather than designing a massive experiment involving many variables and levels, it is usually best to begin with more modest screening designs whose purpose is to determine the variables and levels that need further study.

3. DESIGN PRINCIPLES

Define and apply the principles of power and sample size, balance, replication, order, efficiency, randomization, blocking, interaction, and confounding. (Apply) Body of Knowledge VI.H.3

Once the experiment is planned and carried out, and the outcomes recorded, appropriate analysis is necessary to make final decisions on factors, factor settings, and prediction. Some analysis techniques are described at the end of this section.

Randomization Randomization in experimental design is the ordering of the treatment combinations in a sequence that will reduce the effect of uncontrolled variables that might affect the dependent variable. Randomization will reduce the effect of unwanted nuisance factors that are not part of the experiment but may influence the results. Returning to the surface finish example given earlier, there are eight treatments with five replications per treatment. This produces 40 tests or treatments. The tests from this design should be performed in random order. This would be referred to as a completely randomized design. For the surface finish example, suppose the machine used in the process has some temperature effect; that is, machine temperature increases the longer the machine is running and can possibly affect the surface finish. Furthermore, suppose the treatment combinations are carried out in the order they appear in Figure 39.1. If machine temperature does have an affect on surface finish, and the factor “feed rate” is found to be statistically significant, we can not be sure whether the significant effect is really due to the change in feed rate or due to the temperature of the machine. These two factors could very well be confounded. Confounding in experimental design is the term used to signify that the effect of one independent variable is indistinguishable from the effect of another independent variable or combination of independent variables



(interactions). The 40 tests in the surface finish example may be randomized in two possible ways:

2. Randomize the run order, but once a run is set up, make all five replicates for that run.

Part VI.H.3

1. Number the tests from one to 40 and randomize those numbers to obtain the order in which tests are performed. This is referred to as a completely randomized design.

Although it usually requires more time and effort, the first method is better. To see that this is true, suppose time of day is a noise factor such that products made before noon are different from those made after noon. By randomizing, the time effect of when the product is made is minimized. In this way, if significant effects of the factors are identified, we are more confident that the effect is due to the changes made in the controllable factors than outside, often uncontrollable, factors.

Blocking/Local Control of Error There are many instances when a factor may affect the response of interest but it is not a factor in which we are interested. These factors are often referred to as nuisance factors. For example, suppose the 40 tests in the surface finish example can not be conducted during one shift, but must be carried out over two shifts. In addition, it is believed that the shift may have an effect on surface finish. The team would be concerned about the impact the shift difference could have on the results. Randomization can often reduce the effects of a nuisance factor when there is no way of controlling this factor in practice. If the nuisance factor is known and can be controlled for purposes of experimentation, then the factor can be taken into account during testing. A technique called blocking can be used to eliminate the effect of the nuisance factor. By removing the influence of this factor, the statistical analysis is more likely to reveal whether the factor of interest is truly significant or not. The simplest form of blocking is pairing used to compare two dependent samples (see Chapter 35 for discussion of paired comparisons). Blocking is one form of R. A. Fisher’s concept of local control of error. In general, local control refers to grouping experimental units in such a way that units within the group are homogeneous. This type of control aids in eliminating the variability or noise due to inactive or extraneous factors. Local control also includes the use of covariates when blocking is not possible in an experiment.

Interactions An interaction in experimental design describes the change in the response when two or more factors are interdependent. Interactions are discussed in some detail in Chapter 35 and also further in this chapter with respect to factorial designs.

Efficiency The purpose for conducting a statistically designed experiment is to gain as much relevant information as possible with a minimum amount of cost (cost would include time, money, resources, and so on). Therefore, it is important to construct



Part VI.H.4


and carry out an efficient designed experiment. An efficiently designed experiment is one that will include the minimum number of runs and minimize the amount of resources, personnel, and time that are utilized. Most statistically designed experiments are efficient and economical. Experiments that are not statistically designed are often expensive and inefficient and can often result in waste of resources.

Analysis of Results Designed experiments, when conducted properly, can lead to very reliable results that provide insight into the important factors and optimal level settings. Properly designed experiments and the results of appropriate analysis easily lend themselves to sequential experimentation for more detailed understanding and modeling of the process. The analysis of the results involves some very straightforward but important steps: • Exploratory and graphical analysis. Simple plots and tables of the data can provide insight into the process. • Model fitting. Mathematical models of the form y = f(x) + e are built and provide a relationship between the response and the independent variables. • Fine-tuning the model. Not all independent variables will be significantly related to the response. Several analysis steps can be taken to remove terms from the fitted model that have no significant effect on the response. • Model diagnostics. Assumptions should be verified. The use of plots (such as residual plots) is useful in this step. • Refine the model. This step is necessary if any of the assumptions are violated. Model refitting may be necessary, or a new form of the model investigated. In the next several sections various basic designs and analysis techniques are presented. Complete details on these and other aspects of experimental designs can be found in Montgomery (2009a).

4. ONE-FACTOR EXPERIMENTS

Construct one-factor experiments such as completely randomized, randomized block, and Latin square designs, and use computational and graphical methods to analyze the significance of results. (Analyze) Body of Knowledge VI.H.4



Part VI.H.4

One-factor experiments were first introduced in Chapter 35 when analysis of variance was introduced. In that presentation, the analysis approach for a single factor with several levels (we also referred to this as comparing several treatments) was outlined. The same analysis of variance approach is used when analyzing data from designed experiments. We will now introduce a slightly different form of the model than given previously. The reader will often see both of these models in the literature, so both are presented in this handbook.

Analysis of Variance for One-Factor Experiments As discussed in Chapter 35, if we are interested in comparing more than two levels for a single factor, we must use an appropriately designed experiment and analyze it with analysis of variance (ANOVA) techniques. It is assumed that the experiment has been carried out as a completely randomized design. A general model for the response of interest y involving a levels (or treatments) each with n replicates can modeled as yij = m + t i + e ij for i = 1, 2 ,..., a and j = 1, 2 ,..., n where yij = jth observation from the ith factor level. m = overall mean. t i is the parameter representing the effect of the ith factor level. e ij is the error associated with the jth observation from the ith factor level. In a one-factor design and corresponding experiment, we are trying to determine if there is a significant difference among the a factor levels. Since t i represents the ith factor level and we assume that factor levels do not affect the response differently from one another, our null hypothesis to be tested is H0: t1 = t 2 = … = t a = 0 against the alternative Ha: t i ≠ 0 for at least one i. The analysis of variance approach presented in Section 5 of Chapter 35 applies to the one-factor experimental design, with the model and hypotheses of interest written in a different form. We will now turn our attention to one-factor experiments where blocking should be included.

Randomized Block Designs Often in one-factor experiments there may be a nuisance factor that may have some influence on the results. This factor is considered a blocking factor and should be



Part VI.H.4


included when carrying out the design and analyzing the results. We are not interested in determining whether the levels of the blocking factor are significantly different, but the factor should be included in the experimental design nonetheless. In general, for randomized block designs: • The blocking factor is not modeled as being involved in an interaction with the treatments. Including the blocking factor reduces its effect on the response. This will allows for estimation of factor (independent variable)–level effects. • We are interested in determining whether the factor levels are statistically significantly different, but we are not interested in determining whether the levels of the blocking factor are statistically significantly different. There is already some reason to believe that the blocking factor would influence the results. • If we do not include the blocking variable in a designed experiment and it should be included, we could reach incorrect conclusions about the factor we are investigating.

General Model and Notation Suppose we have a levels of the factor of interest (or treatments), b levels of the blocking factor, and a response denoted y. A general model for the response of interest is yij = m + t i + b j + e ij for i = 1, 2 ,..., a and j = 1, 2 ,.... , b where yij = the response for the ith factor level and the jth level of the blocking factor. m = overall mean. t i = the parameter representing the effect of the ith level of the factor of interest. b j = the parameter representing the effect of the jth level of the blocking factor. e ij = the error associated with the ith level of the factor of interest and the jth level of the blocking factor. In a randomized block design and corresponding experiment, we are trying to determine if there is a significant difference among the a levels of the factor of interest. The null hypothesis of interest is H0: t1 = t 2 = . . . = t a = 0



against the alternative

A general display of the data is given below. Blocks Treatment

1

2

...

b

Treatment totals

1 2 . . . a

y 11 y 21 . . . ya1

y 12 y 22 . . . ya2

... ... . . . ...

y 1b y 2b . . . yab

y 1. y 2. . . . ya.

Block totals

y.1

y.2

...

y.b

y..

Part VI.H.4

Ha: t i ≠ 0 for at least one i.

where • The totals are the sum across that particular row or column. • y.. is the sum of all the observations in the entire experiment. • The total number of observations is given by N = a × b. Note that in this illustration there is exactly one observation per cell (that is, a single replicate, n = 1). The following formulas are necessary to carry out an analysis for any randomized block design with one treatment of interest and one blocking factor. The same notation as presented earlier will be used here to maintain consistency. In randomized block designs, the total sum of squares can be partitioned as follows: SST = SSFactor + SSBlock + SSE The total sum of squares SST is given by a

b

(

SST = ∑ ∑ yij − y.. i=1 j=1

)

2

a

b

= ∑ ∑ yij2 − i=1 j=1

y..2 N

where yij is a single observation. The total degrees of freedom are N – 1. The sum of squares due to different factor levels SSFactor (this was also referred to as SSTreatments in Chapter 35) is a portion of SST that represents the variability explained by or due to the factor levels themselves: a

SS Factor = b∑ ( yi. − y.. ) = i=1

2

1 a 2 y..2 ∑y − b i=1 i. N

The degrees of freedom for the factor of interest would be a – 1. The sum of squares due to different levels of the blocking factor SSBlock is the portion of the SST that represents the variability explained by or due to the different block levels themselves:



b

(

SS Block = a∑ y. j − y..

Part VI.H.4

j=1

)

2

=

1 b 2 y..2 ∑y − a j=1 . j N

The degrees of freedom for the blocking factor are b – 1. The error sum of squares SSE is that portion of the SST that represents the inherent variability and can be found by subtraction: SSE = SST – SSFactor – SSBlock The degrees of freedom for error are (a – 1)(b – 1). As before, the sums of squares will be converted into mean square quantities and the appropriate test statistics calculated. An analysis of variance table can be constructed to summarize the test.

Analysis of Variance (ANOVA) Table The ANOVA table for a randomized block design is shown as Table 39.1. As with the completely randomized design, reject the null hypothesis if F0 > Fa, a–1, (a–1)(b–1) where a is the level of significance, the numerator degrees of freedom are a – 1, and the denominator degrees of freedom are (a – 1)(b – 1). In addition, p values can be used.

Table 39.1 ANOVA table for a randomized block design. Source of variation

SS

df

Factor

SSFactor

a–1

Block

SSBlock

b–1

Error

SSE

(a –1)(b–1)

Total

SST

N–1

MS MS Factor =

SS Factor a−1

MS Block =

SS Block b−1

MS Error =

F F0 =

MS Factor MS E

p value P(F > F0)

SS E ( a − 1)(b − 1)

EXAMPLE 39.1 Four washing solutions are to be compared to study their effectiveness in retarding bacteria growth on a particular type of produce. The analysis is conducted in a lab and the experiment is carried out over a three-day period. The results are recorded and given in the following table: Continued



Continued

1

2

3

1 2 3 4

21 22 31 15

11 21 17 12

12 13 21 8

Part VI.H.4

Day Solution

In this experiment: • The treatment or factor is washing solution. The goal is to determine if there is a statistically significant difference between the four types of solution. • The blocking factor is “day.” Day is the blocking factor because it is believed that the day on which the measurements are taken is a source of variability. It is not a goal to determine if there is a statistically significant difference between the days. We believe that the day does make a difference and that is the reason we used it as a block. • We are not interested in interactions between the washing solution and the day. There are three levels of the blocking factor “day,” so b = 3. There are four levels of washing solution, thus a = 4. An analysis of variance will be conducted to determine whether there is any statistically significant difference between washing solutions. We will test at the five percent level of significance. The hypotheses of interest would be: H 0: t1 = t2 = t 3 = t4 = 0 Ha: t i ≠ 0 for at least one i = 1, 2, 3, 4 The appropriate sums of squares and degrees of freedom can be found using the formulas given previously. The resulting ANOVA table is given in Table 39.2. The analysis of variance was carried out using a reliable statistical software package. The p value is less than our stated level of significance, so the null hypothesis is rejected and we conclude that there is a statistically significant difference between the four washing solutions. The type of washing solution appears to have a significant effect on retarding bacteria growth.

Table 39.2 ANOVA table for the washing solution example. Source of variation

df

SS

MS

F

p value

Washing solution

4–1=3

218.0

218 . 0 = 72 . 67 3

72 . 67 = 6 . 56 11 . 08

0.025

Day

3–1=2

171.5

171 . 5 = 85 . 75 2

Error

(4 – 1)(3 – 1) = 6

66.5

66 . 5 = 11 . 08 6

Total

12 – 1 = 11

456.0



Part VI.H.4

Multiple comparison techniques can be carried out to determine which washing solution is better at retarding (minimizes) bacteria growth.

The Importance of Blocking Blocking can be very important in a designed experiment. If there is an indication that an underlying (nuisance) factor exists that will influence the response of interest, then it should be included in the experiment as a blocking factor. If a nuisance factor is influencing the response and it is not included in the designed experiment as a blocking factor, then the final conclusion (reject or not reject the null hypothesis) could be incorrect. Consider the washing solution example again, but without “day” as the blocking factor.

EXAMPLE 39.2 Suppose “day” was not included in the experiment as the blocking factor in the previous example. The experiment would contain only the factor of interest (washing solution) and becomes a one-factor experimental design. The data table would look like the following: Solution 1 2 3 4

Growth 21 22 31 15

11 21 17 12

12 13 21 8

where • There is one factor, the washing solutions with four levels, that is a = 4. • There are three observations for each type of solution, that is n = 3. The hypotheses of interest are the same as if the blocking factor had been included: H 0: t1 = t2 = t 3 = t4 = 0 Ha: t i ≠ 0 for at least one i = 1, 2, 3, 4 Using the same level of significance, a = 0.05, we can calculate the sum of squares and construct the ANOVA table for a one-factor experimental design (completely randomized design). Here, a = 4, n = 3, and N = an – 1 = 12 – 1 = 11. The ANOVA table is given in Table 39.3. Since the p value is quite large (0.139 > 0.05), we do not reject the null hypothesis and conclude that there is no significant difference between the four washing solutions. Continued



Continued

Source of variation

df

SS

MS

F

p value

4–1=3

218.0

218 . 0 = 72 . 67 3

72 . 67 = 2 . 44 29 . 8

0.139

Error

4(3 – 1) = 8

238.0

238 . 0 = 29 . 8 8

Total

12 – 1 = 11

456.0

Washing solution

Part VI.H.5

Table 39.3 ANOVA table for the washing solution example without blocking.

Remarks The two previous examples demonstrate the importance of considering a possible blocking factor. If there is some underlying factor influencing the response and this factor is not taken into consideration, then the results of the experimental design could be incorrect. If there is any doubt about whether or not a nuisance factor is influencing the results, then it would be in the experimenter’s best interest to include the factor in the experiment and carry out a randomized block design. It is important to note that once the experiment has been carried out with blocking included, we can not reanalyze the experiment as if it were not blocked. Finally, a residual analysis should be conducted in order to test the assumption that the observations are normally and independently distributed with equal variance across factor levels. If there is a serious violation of one or more of the assumptions, a transformation may be necessary. Other methods for dealing with violated assumptions can be found in Devore (2007), Montgomery (2009a), Montgomery and Runger (2006), or Vining and Kowalski (2006).

5. FULL-FACTORIAL EXPERIMENTS Construct full-factorial designs and use computational and graphical methods to analyze the significance of results. (Analyze) Body of Knowledge VI.H.5

In full-factorial experiments all possible combinations of the levels of factors are investigated. The two-factor factorial was introduced in Chapter 35 when discussing the two-way ANOVA. Consider an experiment that involves exactly two factors of interest, A and B, where there are a levels of factor A, b levels of factor B, and n replicates at each combination of A and B. The general model that would describe the response of interest y is given as



Part VI.H.5

yijk = m + t i + b j + (tb )ij + e ijk for i = 1, 2 ,..., a ; j = 1, 2 ,..., b ; and k = 1, 2 ,..., n where yijk = the kth response at the combination of the ith level of A and the jth level of B m = the overall mean effect t i = the parameter for the effect of the ith level of A b j = the parameter for the effect of the jth level of B (tb ) ij = the parameter for the effect of the ijth level of the interaction between A and B e ijk = the error We are interested in the following hypotheses: H0: t1 = t 2 = . . . = t a = 0 (no significant difference between the levels of factor A) Ha: t i ≠ 0, for at least one i H0: b1 = b 2 = . . . = b b = 0 (no significant difference between the levels of factor B) H1: b j ≠ 0, for at least one j H0: (tb )11 = (tb )12 = . . . = (tb ) ab = 0 (no significant interaction between A and B) Ha: (tb ) ij ≠ 0 for at least one i or one j. The sums of squares, degrees of freedom, mean squares, test statistics, and p values can be calculated using a reliable statistical software package. The resulting ANOVA table would look like Table 39.4. It should be noted that the analysis of variance approach is not the only method for testing the significance of the effects and interactions. If each factor has exactly two levels, it is common to examine the results of t-tests on the coefficients representing each factor and interaction. To build a model as recommended, t-tests can be very useful.

Randomized Block Designs and Factorial Designs Before presenting the details of factorial designs at two levels, a distinction should be made between a randomized block design and a two-factor full-factorial design. For example, in an experiment involving two factors A and B, we would be interested in: • Differences between the levels of factor A • Differences between the levels of factor B • Whether a significant interaction between A and B exists



Table 39.4 ANOVA table for two-factor factorial experiment. df

SS

Factor A

a–1

SSA

Factor B

b–1

SSB

(a – 1)( b – 1)

SSAB

Error

ab(n – 1)

SSE

Total

abn – 1

SST

AB interaction

MS

F

p

MS A =

SS A a−1

F0 =

MS A MS E

P(F > F0)

MS B =

SS B b−1

F0 =

MS B MS E

P(F > F0)

SS AB ( a − 1)(b − 1)

F0 =

MS AB MS E

P(F > F0)

MS AB =

MS E =

Part VI.H.5

Source of variation

SS E ab(n − 1)

When is a design that involves two factors a randomized block design and when is it a factorial design? The distinction between the two types of designs comes from the information that is to be gained from conducting the experiment. For instance, consider an experiment that involves two factors. The type of design to use can be determined if the following remarks are kept in mind. Use a randomized block design if: • There is no interest in whether or not significant differences exist between levels of one of the factors (this is the blocking factor). There is some belief that this factor influences the result and by blocking we minimize its influence. • There is little likelihood that there is an interaction between the two factors involved in the study. Use a factorial design if: • There is an interest in determining the differences between the levels of both factors. • There is an interest in determining whether or not a significant interaction between the two factors exists. Of course there are other considerations before conducting the experiment. In addition, it is quite possible to have an experiment that involves more than one factor of interest and one or more blocking factors. Designs sometimes used for experiments involving one independent variable and two blocking factors are referred to as Latin square designs.

Two-Level Factorial Designs A special type of factorial design that receives a great deal of attention is a design where all factors are run at exactly two levels. If there are k factors, then we would say the design is a 2k factorial design. The number of experimental runs



Part VI.H.5


(or observations) is 2k. For example, consider the surface finish illustration given earlier. There are three factors of interest: feed rate, speed, and coolant temperature, each at two levels. A full-factorial design consists of 23 = 8 runs or treatment combinations. The two levels of each factor can be coded as –1 and 1, the low and high level of each factor, respectively. Consider an experiment with two factors A and B, each at two levels: A

B

Low

–1

–1

High

1

1

There would be a total of four combinations, and we would like to determine if A, B, or AB are significant. The combinations can be written in the following table. To obtain the column for the levels of the AB interaction, multiply column A and column B. Run

A

B

AB

1

−1

−1

1

y11, y12, . . .

Responses

2

−1

1

−1

y21, y22, . . .

3

1

−1

−1

y31, y32, . . .

4

1

1

1

y41, y42 , . . .

Suppose there are two replicates for each run and the average response is calculated. Geometrically, we can display the data on a square for the factors as shown in Figure 39.4. Each corner of the square represents a run or treatment combination. The values at the corners represent the average response of each combination. For example, at the low level of A and the high level of B the average response is 24.5. Standard analysis techniques can be applied to the special case of factors with only two levels. We will examine the estimation of the main factors and interaction using an example. 1 24.5

20.0

25.5

27.5

B

–1 –1

Figure 39.4

A

–1

All possible combinations of two factors A and B, with two levels each.



EXAMPLE 39.3

Factor

Low level (–1)

Part VI.H.5

In an article by Lee and Awbi (2004), the authors discuss the effect internal partitioning of office space has on room air ventilation. In the design of modern office buildings, it is important to consider the air quality in a room. For office buildings it is desirable to construct a highly energy-efficient building, often with an open-space floor plan. With open-space construction, internal partitions are introduced to design the office to fit the current needs of the company. With internal partitioning, the layout can easily be restructured for different occupants. However, the air ventilation system is designed for open-space rooms. When interferences are introduced (such as office furniture, wall partitions, and so on) the air quality can be significantly affected. In the study on the effect of internal partitioning on room air quality, three factors are of interest: partition location (PL), partition height (PH), and gap underneath (GU). The partition locations are chosen at 40 percent and 60 percent of the room length from the left end of the room. The partition heights are chosen as 60 percent and 80 percent of the room height. The factor “gap underneath” represents the space between the floor and the bottom of the partition. Gap is set at zero percent of the room height and 10 percent of the room height. One response of interest is ventilation effectiveness e v, a scaleless quantity that is a function of contamination concentration. Larger values of e v indicate better ventilation effectiveness. The tests are conducted on a small scale model test room with the length, width, and height of the room measured in meters. The factors and their levels are given as High level (+1)

Partition length (A)

40%

60%

Partition height (B)

60%

80%

Gap underneath (C)

0%

10%

Suppose a similar experiment was conducted using these factors to test their effect on ventilation effectiveness. The design used was a 2 3 factorial in two replicates, with results given in Table 39.5 (factors are coded). A complete randomization of the treatments for all 16 runs was carried out. Table 39.5

Partitioning effect on ventilation effectiveness.

Treatment

A

B

C

dv

1

–1

–1

–1

2.227, 1.874

2

1

–1

–1

2.134, 2.252

3

–1

1

–1

1.470, 1.404

4

1

1

–1

2.091, 2.270

5

–1

–1

1

2.073, 1.825

6

1

–1

1

2.162, 2.480

7

–1

1

1

1.615, 1.558

8

1

1

1

2.157, 2.169

Continued



Part VI.H.5

Continued

1.5865

2.1630

1 2.1805

1.4370

B

1.9490

2.3210 1 C

2.0505

2.1930

–1

–1 –1

Figure 39.5

A

1

Cube plot for partition length, partition height, and gap underneath.

Graphically, we can display the results using the average response for each treatment. Figure 39.5 displays a cube plot for the three factors: partition length (A), partition height (B), and gap underneath (C).

Main Effects The main effect provides a measure of how each individual factor (main factors such as A, B, and C) affects the response as we move from one level of the factor to the next. The estimated effect for each factor is simply the difference in the average response at the high level of the factor and the average response at the low level of the factor. For example, consider the estimated effect of gap underneath. The average response at the high level (+1) of C (gap underneath) is C+1 =

2 . 073 + 1 . 825 + 2 . 162 + 2 . 480 + 1 . 615 + 1 . 558 + 2 . 1 5 7 + 2 . 169 = 2 . 005 . 8

The average at the low level (–1) of C is C−1 =

2 . 227 + 1 . 874 + 2 . 134 + 2 . 252 + 1 . 470 + 1 . 404 + 2 . 0 9 1 + 2 . 270 = 1 . 965 . 8

The estimated effect of GU on ventilation effectiveness is then ee(C) = C + 1 − C − 1 = 2 . 005 − 1 . 965 = 0 . 04 . (The term “estimated effect” is denoted by ee). The estimated effect for C shows that as the gap underneath the partition is changed from zero percent to 10 percent, the average ventilation effectiveness increases by 0.04. The estimated effects of partition length and partition height are calculated similarly and found to be 0.46 and –0.29, respectively. Main effects plots can be useful for examining the change in the factor effects from the low to high levels. Main effects plots for partition length, partition height, and gap underneath are displayed in Figure 39.6. Continued



Continued

A

B

2.2

Part VI.H.5

It appears that there is a significant difference between the levels of factor A and the levels of factor B, but not necessarily between the levels of factor C.

2.1

Mean ventilation effectiveness

2.0 1.9 1.8 –1

1

–1

1

C 2.2 2.1 2.0 1.9 1.8 –1

Figure 39.6

1

Main effects plot for the air quality example.

Using this definition of main effect, the larger the absolute value of the main effect, the more influence that factor has on the quality characteristic. It is possible that the perceived difference between high and low results is not statistically significant. This would occur if the experimental error is so large that it would be impossible to determine whether the difference between the high and low levels is due to a real difference in the dependent variable or due to experimental error. This may be determined by using ANOVA procedures and/or t-tests. For analysis of data from an experiment, the null hypothesis is that changing the factor level does not make a difference in the dependent variable. The a-risk is the probability that the analysis will show that there is a significant difference when there is not. The b-risk is the probability that the analysis will show that there is no significant difference when there is. The power of the experiment is defined as 1 – b, so the higher the power of the experiment, the lower the b -risk. In general, a higher number of replications or a larger sample size provides a more precise estimate of experimental error, which in turn reduces the b -risk.



Part VI.H.5

Interaction Effects As discussed previously, interactions may exist between the factors of interest, and this interaction effect must be determined as was done with the main effects. The interactions in our example include the two-factor interactions and the threefactor interaction: partition length by partition height (AB), partition length by gap underneath (AC), partition height by gap underneath (BC), and partition length by partition height by gap underneath (ABC). Again, we want to find the average difference in the response between the high and low level of each interaction. What is considered a “high” and “low” level for an interaction? The levels of the interactions are simply the results of the levels of the main effects. In coded form we can label the high and low levels of each interaction simply by multiplying the levels of each factor involved in the interaction. For example, if A is set at its low level (A = –1) and B is set at its high level (B = 1), then the corresponding level of the interaction AB would be –1 (since –1 × 1 = –1). This is simply a label that is convenient for determining the low and high levels of each interaction and is a result of the geometry of the design. The table of contrasts for the main effects and interactions for the 23 full-factorial design is given as Table 39.6. Notice that any column multiplied by itself results in a column of +1’s only. When a column consists of 1’s only, it is called the identity column and denoted I. For example, A × A = I. The estimated effects of the interactions can be easily calculated. For example, the average response at the high level of the interaction AB (partition length and partition height) is AB + 1 =

2 . 227 + 1 . 874 + 2 . 091 + 2 . 270 + 2 . 073 + 1 . 825 + 2 .11 57 + 2 . 169 = 2 . 086 . 8

The average response at the low level of AB is AB − 1 =

Table 39.6

2 . 134 + 2 . 252 + 1 . 470 + 1 . 404 + 2 . 162 + 2 . 480 + 1 .66 15 + 1 . 558 = 1 . 884 . 8

Main effect and interaction table of contrasts for the ventilation example.

Treatment

A

B

C

AB

AC

BC

ABC

dv

1

–1

–1

–1

1

1

1

–1

2.227, 1.874

2

1

–1

–1

–1

–1

1

1

2.134, 2.252

3

–1

1

–1

–1

1

–1

1

1.470, 1.404

4

1

1

–1

1

–1

–1

–1

2.091, 2.270

5

–1

–1

1

1

–1

–1

1

2.073, 1.825

6

1

–1

1

–1

1

–1

–1

2.162, 2.480

7

–1

1

1

–1

–1

1

–1

1.615, 1.558

8

1

1

1

1

1

1

1

2.157, 2.169



The estimated effect of the AB interaction is then

The remaining interaction effects can be estimated similarly. The estimated effects for all of the main effects and interactions are given in Table 39.7. Interaction plots are often useful for examining the two-factor interactions. Recall that interaction plots are discussed in Chapter 35. The interaction plots for our example are given in Figures 39.7 through 39.9. Based on the interaction plots, it is possible that a significant interaction exists between partition length (A) and partition height (B). There may be a significant interaction between partition height (B) and gap underneath (C), but that is not definitive. Lastly, there does not appear to be a significant interaction between partition length (A) and gap underneath (C). Again, the plots may be somewhat subjective (especially in Figure 39.8), and more statistically-based evidence is needed.

Part VI.H.5

ee(AB) = AB+1 – AB –1 = 2.086 – 1.884 = 0.202.

Table 39.7 Estimated effects for the air quality example. Factor

Estimated effect (ee)

Factor

Estimated effect (ee)

A

0.456

BC

0.016

B

–0.287

AC

0.026

C

0.040

ABC

–0.099

AB

0.202

Mean of ventilation effectiveness

2.3

A –1 1

2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 –1

1 B

Figure 39.7

Interaction plot for partition length and partition height.



Part VI.H.5


2.3

A –1 1

2.2 2.1 2.0 1.9 1.8 1.7 –1

1 C

Figure 39.8

Interaction plot for partition length and gap underneath.


2.15

B –1 1

2.10 2.05 2.00 1.95 1.90 1.85 1.80 –1

1 C

Figure 39.9

Interaction plot for partition height and gap underneath.

Testing for Statistical Significance of the Effects We can determine whether the effects are statistically significant using the analysis of variance approach, a model-fitting approach, or both. We will first look at the analysis of variance approach.

Analysis of Variance Approach In the analysis of variance approach, sums of squares are calculated for each effect and the experimental error. The degree of freedom for factors with exactly two



SSFactor = n2k–2(ee)2 where n is the number of replicates and k is the number of factors. Test statistics can be calculated for each factor and each interaction. All of this information can be summarized in an analysis of variance table. The ANOVA table for our example is given in Table 39.8, where degrees of freedom for error are 2k (n – 1) and total degrees of freedom are n2k – 1. Based on the results of the analysis of variance, it appears that factors A and B and the interaction AB are significant. This is evident by the small p values for each of these terms. Based on these results, the analysis should be carried out again but with only the significant terms included. The results for the new analysis are given in Table 39.9. Again, the main effects of A and B and the interaction AB are statistically significant. From the ANOVA table we also see that sˆ 2 = MSE = 0.01832.

Part VI.H.5

levels (–1, +1) is 1 (the number of levels minus 1 as with all factorial designs). The sums of squares for each factor can be found easily using the estimated effects and can be shown to be

Table 39.8 ANOVA table for the ventilation example. Source

df

SS

MS

F

p

A

1

0.84135

0.84135

39.48

0.000

B

1

0.32862

0.32862

15.42

0.004

C

1

0.00628

0.00628

0.29

0.602

AB

1

0.16221

0.16221

7.61

0.025

AC

1

0.00098

0.00098

0.05

0.836

BC

1

0.00278

0.00278

0.13

0.727

ABC

1

0.03930

0.03930

1.84

0.211

Error

8

0.17048

0.02131

Total

15

1.55199

Table 39.9

ANOVA table for the ventilation example.

Source

df

SS

MS

F

p

A

1

0.84135

0.84135

45.93

0.000

B

1

0.32862

0.32862

17.94

0.001

AB

1

0.16221

0.16221

8.86

0.012

Error

12

0.21982

0.01832

Total

15

1.55199



Part VI.H.5


We can determine which levels of the factors will result in large values for ventilation effectiveness. More details on determining these levels are provided later in this chapter. We will now present the model-fitting approach. The model-fitting approach is simply the regression analysis procedure described in Chapter 36. It is used to fit a model relating the dependent variable (response) and independent variables (factors). Multiple linear regression was briefly introduced in Chapter 36.

Model-Fitting Approach Model fitting (regression analysis) and ANOVA are not necessarily separate approaches. An analysis of variance is often reported when the regression approach is used to study the effects of the factors and interactions on the response. A model relating the independent variables and dependent variable (response) can be given by y = b 0 + b 1 x1 + b 2 x2 + ... + b k xk + e where y is the response of interest and x1, x2, . . . , xk represent the independent variables. In our factorial designs, the independent variables are our factors such as A, B, C, and the interactions among the factors AB, AC, BC, and ABC. To illustrate, we can let x1 represent factor A, x2 represent factor B, and so on. Note that the convention to let x1x2 represent the AB interaction, for example, is used. The coefficients b i on each term can be tested using t-tests as done in Chapter 36. The null hypothesis of interest is H0: b i = 0 for all i. Results of the t-test for the air quality example are given in Table 39.10. The “Effect” column displays the estimated effects for the main effects and interactions. Based on the p values for the t-tests we again conclude that partition length, partition height, and the interaction between the two factors are significant. The analysis should be rerun involving only the terms found significant. A model relating ventilation effectiveness to partition length, partition height, and

Table 39.10 Term Constant

t-tests for factors and interactions for the air quality example. Effect —

Coef

SE Coef

t

p

1.9851

0.03649

54.39

0.000

A

0.4586

0.2293

0.03649

6.28

0.000

B

–0.2866

–0.1433

0.03649

–3.93

0.004

C

0.0396

0.0198

0.03649

0.54

0.602

AB

0.2014

0.1007

0.03649

2.76

0.025

AC

0.0156

0.0078

0.03649

0.21

0.836

BC

0.0264

0.0132

0.03649

0.36

0.727

–0.0991

–0.0496

0.03649

–1.36

0.211

ABC



the interaction can now be fit. The column labeled “Coef” provides the estimates of the coefficients in the regression model

where x1 represents partition length, x2 represents the partition height, and x1x2 represents the interaction between the two factors. It should also be noted that the coefficient estimates are one-half of the estimated effects. The fitted model above is in coded form. That is, if we want to make predictions for certain levels of the factors, we would use the notation (–1, 1) to plug into the equation. For example, if we wanted to predict the ventilation effectiveness for the low levels of A and B, we would let x1 = –1 and x2 = –1 in our fitted model:

Part VI.H.5

yˆ = 1 . 9851 + 0 . 2293 x1 − 0 . 1433 x2 + 0 . 1007 x1 x2

yˆ = 1 . 9851 + 0 . 2293 x1 − 0 . 1433 x2 + 0 . 1007 x1 x2 = 1 . 98 5 1 + 0 . 2293(− 1) − 0 . 1433(− 1) + 0 . 1007(− 1)(− 1) = 2 .00 The model can also be written in terms of the actual levels of the factors. It is recommended that the model fitting be done using a reliable statistical software package. The model using the actual levels can be shown to be yˆ = 7 . 208 − 0 . 0829A − 0 . 0907 B + 0 . 0015AB. The predicted value when partition length and partition height are at their low levels is found by replacing A and B with the actual levels of the factors. For A at its low level (40) and B at its low level (60), the predicted value is yˆ = 7 . 208 − 0 . 0829A − 0 . 0907 B + 0 . 0015AB = 7 . 208 − 0 . 0829( 4 0) − 0 . 0907(60) + 0 . 0015( 40)(60) = 2 . 05 . The difference between this estimate and the one from the model in coded units is due strictly to round-off error. Either model can be used to fit the data. In addition, the actual levels in this example are left as percentage values such as 40 and not converted to decimal form such as 0.40. This was only by choice. Using a statistical package we could have stated the lower level and upper level of partition length as 0.40 and 0.60, respectively, but we chose to use 40 and 60. The same main effects and interaction would still be found to be significant.

Residual Analysis Once the model has been refined so that it contains only those terms that are statistically significant, the three assumptions of normality, independence, and constant variance should be investigated. If the order in which the treatments were carried out was not recorded, the independence assumption will be difficult to verify. Hopefully, by randomizing all 16 runs while all extraneous factors are held



Part VI.H.5


constant there is no significant problem with dependency. Again, it is desirable to actually be able to check this assumption. Recall that the residuals are defined as ei = yi – yˆ i where the predicted values are found as shown previously. The 16 residuals can be calculated and analyzed through residual plots. The normal probability plot of the residuals is shown in Figure 39.10. The residuals appear to fall along a straight line, so the normality assumption does not appear to be violated. The residuals plotted against the significant factors are displayed in Figure 39.11 and Figure 39.12. There does not appear to be a problem with constant variance across the factor level.

99 95

Percent

90 80 70 60 50 40 30 20 10 5 1 –0.3

Figure 39.10

–0.2

–0.1

0.0 Residual

0.1

0.2

0.3

Normal probability plot of the residuals for the air quality example.

0.2

Residual

0.1

0.0

–0.1

–0.2 –1.0

Figure 39.11

–0.5

0.0 A

0.5

1.0

Residuals plotted against levels of factor A (partition length).



0.2

Part VI.H.5

Residual

0.1

0.0

–0.1

–0.2 –1.0

Figure 39.12

–0.5

0.0 B

0.5

1.0

Residuals plotted against factor B (partition height).

1.0 1.9 1.6

1.7

2.1

B

0.5

0.0

1.8

–0.5

2.2

2.0

–1.0 –1.0

Figure 39.13

–0.5

0.0 A

0.5

1.0

Contour plot for the air quality example.

Determining the Optimal Settings of the Important Factors Now that the significant terms are identified and the necessary assumptions have been shown to be satisfied, the next step is to determine the optimal settings for the significant factors. In our example, a goal is to maximize the ventilation effectiveness. There are several ways to determine these settings. We will discuss two graphical methods. A useful graphical display of the fitted model is a contour plot. The contour plot for our fitted model in coded form is shown in Figure 39.13. A contour plot



Part VI.H.5


displays the predicted response over the range of the significant factors. Notice that the contour lines are curved; this is the result of a significant interaction between the two factors. For our example, we see that the lower right-hand corner of the contour plot displays higher values of ventilation effectiveness. If the goal is to maximize ventilation effectiveness, it appears that factor A (partition length) should be set at its high level (60 percent) while factor B (partition height) should be set at its low level (60 percent). Main effects plots and interaction plots are also useful graphical displays of the results. Since the two main effects found significant are involved in a significant interaction, it is the interaction plot that should be examined. In fact, if the factors are involved in a significant interaction but only the main effects plots of these factors are examined, it is possible to choose less-than-optimal settings of the factors. Recall the interaction plot of factors A and B displayed in Figure 39.7. On this figure we see that the highest ventilation effectiveness occurs at the low level of B (partition height) and high level of A (partition length). The reader is encouraged to consult Devore (2007), Montgomery and Runger (2006), Montgomery, Runger, and Hubele (2006), or Vining and Kowalski (2006) for more details on methods for determining acceptable levels of the significant factors.

2k Designs with a Single Replicate Often it is not possible or economical to obtain more than a single replicate for a designed experiment (that is, n = 1). When this is the case it is not possible to test the significance of all of the effects. There is no internal estimate of error since there is no replication. Specifically, there are no degrees of freedom left over for error in order to estimate the process variability s 2. The total degrees of freedom for a design with a single replicate are 2k – 1. Each main effect and interaction is given one degree of freedom. For example, suppose that in our ventilation effectiveness example we only have one replicate. The total number of runs would be eight and the total degrees of freedom would be 2k – 1 = 8 – 1 = 7. Furthermore, there are three main effects (A, B, and C) and four interactions (AB, AC, BC, and ABC). Since every effect has one degree of freedom, all of the degrees of freedom are used. There are no degrees of freedom for error. t-tests and the analysis of variance method can not be carried out. To address this issue, there are several approaches that can be employed. The approaches are often based on the sparsity-of-effects principle. That is, an assumption is made that some higher-order interactions are negligible (orders higher than two-factor interactions) and the system being investigated is believed to be dominated by the main effects and the low-order interactions. Under this assumption the degrees of freedom for the higher-order interactions are pooled into error degrees of freedom. Any sums of squares these interactions may have had get pooled into error sum of squares. If there is any indication that one or more of the higher-order interactions is significant, then the pooling approach is not appropriate. A different method of analysis that is often used is examination of a normal probability plot of the estimated effects. This approach was suggested by Daniels (1959) and is available in most statistical software packages. Effects that are not significant (or are negligible) are said to be normally distributed with mean zero and variance s 2. When



EXAMPLE 39.4 Consider the air quality scenario presented earlier. Suppose that only one replicate was obtained for each of the eight runs. Data typical of this experiment are shown in Table 39.11. The estimated effects for the main factors and all of the interactions can still be calculated using the formulas given previously. The estimated effects are then plotted on a normal probability plot (sometimes a standardized value of the effects will be plotted). The normal probability plot of the estimated effects is displayed in Figure 39.14.

Table 39.11

Part VI.H.5

plotted on a normal probability plot, estimated effects that are negligible will tend to fall along a straight line. The negligible effects are pooled into error and the degrees of freedom assigned to error.

A single replicate of the air quality example.

Treatment

A

B

C

1

–1

–1

–1

2

1

–1

–1

3

–1

1

–1

4

1

1

–1

5

–1

–1

6

1

–1

7

–1

8

1

AB

AC

BC

ABC

1

1

1

–1

2.135

–1

–1

1

1

2.015

–1

1

–1

1

1.520

1

–1

–1

–1

1.999

1

1

–1

–1

1

1.998

1

–1

1

–1

–1

2.103

1

1

–1

–1

1

–1

1.624

1

1

1

1

1

1

2.135

dv

Normal plot

Normal % probability

99 95 90 80 70 50 30 20 10 5

AB A

B

1 –0.24

–0.12

0.00

0.13

0.25

Standardized effect

Figure 39.14

Normal probability plot of the estimated effects for the air quality example. Continued



Part VI.H.6

Continued

The normal probability plot of the effects (the effects have been “standardized”) indicates that the main effects A and B and the interaction AB may be significant. An analysis could then be carried out on A, B, and AB. An analysis is conducted on the three terms with all other terms pooled into error. The results of t-tests on the effects are shown in Table 39.12. Model fitting and residual analysis can be completed similarly to the case of more than one replicate. An analysis of variance can also be conducted with A, B, and the AB interaction as the only terms in the model.

Table 39.12 Term Constant

t-test results for the air quality example. Effect —

Coef

SE Coef

t

p

1.9411

0.02953

65.72

0.000

A

0.2438

0.1219

0.02953

4.13

0.015

B

–0.2432

–0.1216

0.02953

–4.12

0.015

0.2512

0.1256

0.02953

4.25

0.013

AB

Remarks The methods and procedures outlined in this section can be used for any number of factors at two levels each. A drawback to the use of 2k full-factorial designs is that the design size becomes prohibitively large as the number of factors increases. For example, even if there are only seven factors each at two levels, the number of experimental runs would be 27 = 128, without replication. It is not unusual, especially in screening experiments, to have six, seven, or more factors of interest being investigated. In these cases, it is often very useful to run experiments involving fractions of the full-factorial design. These designs are commonly referred to as fractional factorial designs and are discussed in the next section.

6. TWO-LEVEL FRACTIONAL FACTORIAL EXPERIMENTS

Construct two-level fractional factorial designs (including Taguchi designs) and apply computational and graphical methods to analyze the significance of results. (Analyze) Body of Knowledge VI.H.6



Half-Fractions of 2k Designs Half-fractions of a 2k are designs that consist of half of the standard 2k design. Halffractions are usually denoted 2k–1 (one-half of the 2k = 2k/2 = 2k–1).

Part VI.H.6

Fractional factorial designs are those where only a fraction of the full-factorial design is used. Fractional factorial designs are economic alternatives to the full-factorial designs as the number of factors increases. Screening experiments often involve a large number of factors, so full-factorials are not always practical or economical.

EXAMPLE 39.5 Consider an experiment with six factors each at two levels. A full-factorial design consists of 26 = 64 combinations or runs. A full 26 experimental design is in many instances prohibitively large. However, 32 experimental runs may be more economical. In this case, we can choose one-half of the runs from the full 26 . The resulting design is referred to as a 26 –1 design. Runs selected from the full-factorial are not chosen at random. More on this later in this section.

Higher-Level Fractional Factorials In some instances, a half-fraction may still be too large and impractical. It may be more economical to use designs that are one-fourth the size of the full-factorial or possibly one-eighth the size of the full-factorial. The general notation for a fractional factorial design is denoted as 2k–p where 2p represents the fraction of the full-factorial.

EXAMPLE 39.6 A screening experiment is going to be conducted involving 10 factors each at two levels. A full-factorial design with a single replicate would still require 210 = 1024 runs. The experimenters can afford to do no more than 40 runs initially for screening. A fraction of the full-factorial that could be used and still meet the size requirements would be a 2 10–5 design, which would require only 32 experimental runs (2 10–5 = 2 5 = 32).

Design Resolution When conducting an experiment with k factors, we are not only interested in the significance of each factor, but also the interactions between the factors. If a fullfactorial could be implemented, then all main effects of interest and all two-factor interactions are fully estimable. However, when employing fractional factorial designs, the design size is reduced, and not all the interactions of interest may be



Part VI.H.6


estimable separately from main effects or other interactions. Some of the interactions and/or main effects may be confounded or aliased with one another, making it difficult to determine which factor or interaction is truly significant.

EXAMPLE 39.7 A study is going to be conducted involving three factors each at two levels. Suppose we can only afford to conduct four treatments and not the eight that would make up a full 2 3 factorial design. A half-fraction of the 2 3 design would seem like a natural choice and would be called a 2 3–1 design. A 2 3–1 design as well as all columns for the interactions are given in Table 39.13. Notice that the column for factor C and the column for the AB interaction are identical. We would say that factor C is aliased or confounded with the AB interaction, that is, C = AB. Also notice in the table that the column for the ABC interaction contains only the high level of the interaction. We would say that ABC is the equal to the identity column (I = ABC).

Table 39.13

Contrasts for a 2 3–1 design.

Run

A

B

C

AB

AC

BC

ABC

1

–1

–1

1

1

–1

–1

1

2

1

–1

–1

–1

–1

1

1

3

–1

1

–1

–1

1

–1

1

4

1

1

1

1

1

1

1

The identity column will be very useful in fractional factorial designs. If any column in the design is multiplied by the identity column, the result is the original column. For example, A × I = A. The ABC interaction is a generator and would be used to generate the column for one of the main factors. ABC is often referred to as a word. An important characteristic of fractional factorial designs is the defining relation. The defining relation is one that contains all possible “words” whose signs do not change in the experiment. For example, from Table 39.13 we see that the interaction column ABC consists of all +1’s. Therefore, ABC would be a word in the defining relation. Since it is the only column with the signs unchanged, it is the only word in the defining relation. In this problem, our defining relation would be I = ABC. All aliases are found through the defining relation. For example, the alias for factor A is A · I = A · ABC = BC. Therefore, factor A is aliased with the BC interaction. The other aliases are found similarly: B · I = B · ABC = AC



C · I = C · ABC = AB

Part VI.H.6

The configuration in Table 39.13 guarantees that none of the main factors have identical columns (therefore they are not aliased or confounded with one another). But main effects are aliased with two-factor interactions. If it is believed that the AB interaction may be significant, then a different design (with more runs) would have to be used. The 23–1 design is said to be of resolution III. Resolution III designs are those where main effects are aliased with two-factor interactions. More on this later in this section. Resolution IV designs or higher are desirable, since they guarantee that the main effects will be clear of (not aliased with) other main effects and clear of two-factor interactions. The obvious drawback to resolution IV designs is that two-factor interactions are aliased with other two-factor interactions. Suppose we carry out the 24–1 design, analyze the results, and determine that all main effects and the two-factor interaction AB are found to be statistically significant. With resolution IV designs, we do not know for sure that AB is truly significant or if the two-factor interaction it is aliased with (here AB = CD) is significant. There are methods for breaking these aliases that involve adding a subset of new experimental runs. See Box, Hunter, and Hunter (2006) or Montgomery (2009a) for more details on breaking these aliases (also referred to as “de-aliasing”).

EXAMPLE 39.8 A study is going to be conducted involving four factors each at two levels. Suppose we can only afford to conduct eight treatments and not the 16 that would make up a full 24 factorial design. A half-fraction of the 24 design would seem like a natural choice. The half-fraction of the 24 is the 24–1 design and would contain 2 3 = 8 runs. One possible fraction is displayed in Table 39.14.

Table 39.14

Half-fraction of a 24 factorial design.

Run

A

B

C

D

1

–1

–1

–1

–1

2

1

–1

–1

1

3

–1

1

–1

1

4

1

1

–1

–1

5

–1

–1

1

1

6

1

–1

1

–1

7

–1

1

1

–1

8

1

1

1

1 Continued



Part VI.H.6

Continued

In this study a full 2 3 design was constructed for factors A, B, and C. Column D was generated from the three-factor interaction ABC, that is, D = ABC. In this example ABC is the generator and the defining relation is I = ABCD. This design is said to be of resolution IV. Resolution IV designs are those where main effects are aliased with three-factor interactions, and two-factor interactions are aliased with other two-factor interactions. Using the defining relation I = ABCD we can obtain all of the aliases. For the main effects: A = BCD B = ACD C = ABD D = ABC For the two-factor interactions: AB = AB ∙ I = AB ∙ ABCD = CD AC = AC ∙ I = AC ∙ ABCD = BD AD = AD ∙ I = AD ∙ ABCD = BC

The following are some of the properties for experimental design: 1. Resolution III designs have main effects confounded with two-factor interactions. 2. Resolution IV designs have main effects confounded with three-factor interactions and two-factor interactions confounded with each other. 3. Resolution V designs have two-factor interactions confounded with three-factor interactions only.

EXAMPLE 39.9 Consider an experiment involving six factors where only 16 runs can be used. A fullfactorial design in 16 runs is a 24 design. Our design is referred to as a 26 –2 fractional factorial design. We could construct a 24 full-factorial design for four of the six factors, but the remaining two factor columns would have to be generated. Let A, B, C, D, E, and F represent the six factors. Suppose a full-factorial design is constructed for A, B, C, and D. It can be shown that two generators needed for E and F could be E = ABC and F = BCD. The resulting defining relation would be I = ABCE = BCDF = ADEF. The last “word,” ADEF, is found by multiplying the two original generators, ABCE and BCDF (see Box, Hunter, and Hunter [2006] or Montgomery [2009a] for more details). The resolution of this design is IV.



I = ABCDF = ABDEG = CEFG.

Part VI.H.6

In general, the resolution of a design can always be determined from a complete defining relation. By definition, the resolution of a design is equal to the length of the smallest word in the defining relation. For example, consider a 27–2 design with factors A, B, C, D, E, F, and G. The complete defining relation for this design can be shown to be

The length of the smallest word is four, so the design is of resolution IV. There are numerous approaches and methods involving fractional factorial designs. The reader is encouraged to see Box, Hunter, and Hunter (2006), Ledolter and Swersey (2007), and Montgomery (2009a) for complete details and examples of full and fractional factorial designs and their applications.

Taguchi Robustness Concepts Robustness means resistance to the effect of variation of some factor. For example, if brand A chocolate bar is very soft at 100° F and brittle at 40° F, and brand B maintains the same level of hardness at these temperature extremes, it could be said that brand B is more robust to temperature changes in this range. If a painting process produces the same color on moist wood as dry wood, the color is robust to variation in moisture content. The changes in temperature and humidity are referred to as noise. Producing products that are robust to noise of various kinds is clearly desirable. The Japanese engineer Genichi Taguchi is credited with developing techniques for improving robustness of products and processes. One approach to improving robustness is illustrated in Table 39.15. As usual, the average value for each run is calculated and is labeled y–. In addition, the standard deviation of the values in the run is calculated and shown in the column labeled “S.” Now the experimenter can complete the usual main effects calculations to determine the levels of each of the factors that will optimize the response value y. In addition, the main effects calculations can be run using the values in the S

Table 39.15

Example using signal-to-noise ratio. y–

S

25

31.5

5.7

39

38

41.5

4.0

41

48

43

46.5

5.8

35

31

32

34

33.0

1.8

–

62

68

63

69

65.5

3.5

–

+

25

33

36

21

28.8

6.9

+

+

–

58

54

58

60

57.5

2.5

+

+

+

39

35

42

45

40.3

4.3

A

B

C

Replications

–

–

–

34

29

38

–

–

+

42

47

–

+

–

54

–

+

+

+

–

+



Part VI.H.6


column to find the levels of each of the factors that will minimize the S value. If these two combinations of levels do not agree, then a compromise between optimizing the response and minimizing the variation must be made. One way to approach the compromising process is through what Taguchi called the signal-tonoise ratio. If it is desirable to maximize y, the signal-to-noise ratio may be calculated for each run using S/ N =

y . S

The main effects may then be calculated, using the S/N ratios to find the best levels for each factor. If, instead, it is desirable to make y as small as possible, the signal-to-noise ratio can be defined as S/ N =

1 . yS

If it is desirable to make y as close to some nominal value N as possible, the signalto-noise ratio can be defined as S/ N =

1 . y−N S

Note that the signal-to-noise ratio is an attempt to find a useful compromise between two competing goals, optimizing y and minimizing S. It does not necessarily accomplish either of these goals, so it should be used with a bit of judgment. See Box (1988) for more details discussing signal-to-noise ratios and how a significant factor could go undetected when significant changes occur in both the average and variance. Another technique Taguchi used for improving robustness is called the inner/ outer array design. In this procedure, the uncontrollable factors—those factors that the experimenter either can not or chooses not to control—are placed in separate columns next to the controllable factors, as shown in Table 39.16. In this example, hardness of the steel and the ambient temperature are the uncontrollable factors. These factors could conceivably be controlled by putting the machine in an environmental enclosure and putting a tighter specification on the steel, but the experimenter chooses not to do either of these. Instead, anticipated extremes of hardness and ambient temperature are used for the experiment to determine settings of the controllable variables that will minimize variation in the output quality characteristic. When the first run of the design in Table 39.16 is executed, the feed, speed, and coolant temperature are all set at their low levels. One part is made with low-hardness steel and low ambient temperature, and the value of the quality characteristic is entered in the spot labeled “a.” When all 32 values have been entered, the averages and standard deviations are calculated. In this inner/outer array approach, the design intentionally causes perturbations in the uncontrollable factors to find level combinations for the controllable factors that will minimize



Table 39.16

Illustration of inner and outer arrays. Outer array Hardness

–

–

+

+

Coolant temp. Ambient temp. –

+

–

+

Feed

Speed

–

–

–

–

–

+

–

+

–

–

+

+

+

–

–

+

–

+

+

+

–

+

+

+

y–

S

a

Part VI.H.6

Inner array

the variation in the quality characteristics under the anticipated hardness and ambient temperature variation. Motivation for the use of crossed-array designs is simple. All control-by-noise interactions can be estimated clear of any other factors or interactions. If controlby-noise interactions are significant, then it may be possible to find settings of the controllable variables that are robust to variations in the uncontrollable variables. As a result, it may be possible to minimize the variability transmitted by the noise factors to the response. If no control-by-noise interactions are found significant, then there is no robust design problem. There are drawbacks to the used of crossed-array designs. As the number of control and/or noise variables increases, the number of runs needed becomes excessive. In addition, even with a large number of runs the crossed-array designs do not readily accommodate potentially important control-by-control interactions. Combined array designs are an efficient alternative to the crossed-array designs presented by Taguchi. Combined array designs are those that combine the controllable and uncontrollable variables into a single design array, such as a 2k or 2k–p design. There are a number of possible combined array designs that are highly efficient for the robust design problem, allowing for estimation of the control-by-control interactions and control-by-noise interactions. For more discussion on the merits and drawbacks to Taguchi’s approach to robust design see, for example, Nair (1992), Box, Bisgaard, and Fung (1988), Hunter (1985), Montgomery (2009a), Myers and Montgomery (2002), and Pignatiello and Ramberg (1992).

Designed Experiments and Statistical Control There has been considerable debate about the use of designed experiments in industry if the process under investigation is not known to be in statistical control. Some



Part VI.H.6


researchers have argued that the process must be in statistical control before conducting legitimate industrial experiments while others have argued that statistical control is not necessary. Research by R. A. Fisher first published in 1925 showed that statistical control was not a prerequisite for implementing designed experiments when replication, blocking, and randomization are key components of the experimentation. Arguments have been made to the effect that Fisher’s results, while applicable in agricultural experiments, do not apply in industrial settings. Anyone involved in conducting experiments should read the article and discussion that appeared in Quality Engineering (volume 20, no. 2, 2008). Statistical control and designed experiments are discussed in detail by Søren Bisgaard, with discussion of his article provided by G. Geoffrey Vining, Thomas P. Ryan, George E. P. Box, Donald J. Wheeler, and Douglas C. Montgomery. The article and discussions are a must read for practitioners and researchers alike and provide numerous references for further reading.

SUMMARY OF PART VI The methods and tools presented in Part VI, although complex in their details, provide a basis for what is sometimes referred to as management by fact. The proper use of this content will permit the user to determine how best to collect and analyze data so that sound decisions are possible.



Part VII Appendices Appendix A Appendix B Appendix C Appendix D

Appendix E Appendix F Appendix G Appendix H Appendix I Appendix J Appendix K Appendix L Appendix M Appendix N Appendix O Appendix P Appendix Q

ASQ Certified Quality Engineer (CQE) Body of Knowledge Control Limit Formulas Constants for Control Charts Statistical Tolerance Factors for at Least 99 Percent of the Population (“k-Values”) Standard Normal Distribution for Selected Z-Values Areas under Standard Normal Distribution to the Left of Z-Values F Distribution F0.10 F Distribution F0.05 F Distribution F0.01 Binomial Distribution Chi-Square Distribution Exponential Distribution Poisson Distribution Median Ranks Normal Scores Values of t Distribution Selected National and International Quality System Standards

575



Appendix A ASQ Certified Quality Engineer (CQE) Body of Knowledge

T

he topics in this Body of Knowledge include subtext explanations and the cognitive level at which the questions will be written. This information will provide useful guidance for both the Exam Development Committee and the candidate preparing to take the exam. The subtext is not intended to limit the subject matter or be all-inclusive of that material that will be covered in the exam. It is meant to clarify the type of content that will be included on the exam. The descriptor in parentheses at the end of each entry refers to the maximum cognitive level at which the topic will be tested. A complete description of cognitive levels is provided at the end of this document. I. Management and Leadership (15 Questions) A. Quality Philosophies and Foundations Explain how modern quality has evolved from quality control through statistical process control (SPC) to total quality management and leadership principles (including Deming’s 14 points), and how quality has helped form various continuous improvement tools including lean, six sigma, theory of constraints, etc. (Remember) B. The Quality Management System (QMS) 1. Strategic planning. Identify and define top management’s responsibility for the QMS, including establishing policies and objectives, setting organization-wide goals, supporting quality initiatives, etc. (Apply) 2. Deployment techniques. Define, describe, and use various deployment tools in support of the QMS: benchmarking, stakeholder identification and analysis, performance measurement tools, and project management tools such as PERT charts, Gantt charts, critical path method (CPM), resource allocation, etc. (Apply) 3. Quality information system (QIS). Identify and define the basic elements of a QIS, including who will contribute data, the kind of data to be managed, who will have access to the data, the level of flexibility for future information needs, data analysis, etc. (Remember) 576


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C. ASQ Code of Ethics for Professional Conduct. Determine appropriate behavior in situations requiring ethical decisions. (Evaluate) D. Leadership Principles and Techniques. Describe and apply various principles and techniques for developing and organizing teams and leading quality initiatives. (Analyze) E. Facilitation Principles and Techniques. Define and describe the facilitator’s role and responsibilities on a team. Define and apply various tools used with teams, including brainstorming, nominal group technique, conflict resolution, force-field analysis, etc. (Analyze) F. Communication Skills. Describe and distinguish between various communication methods for delivering information and messages in a variety of situations across all levels of the organization. (Analyze) G. Customer Relations. Define, apply, and analyze the results of customer relation measures such as quality function deployment (QFD), customer satisfaction surveys, etc. (Analyze) H. Supplier Management. Define, select, and apply various techniques including supplier qualification, certification, evaluation, ratings, performance improvement, etc. (Analyze) I. Barriers to Quality Improvement. Identify barriers to quality improvement, their causes and impact, and describe methods for overcoming them. (Analyze) II. The Quality System (15 Questions) A. Elements of the Quality System. Define, describe, and interpret the basic elements of a quality system, including planning, control, and improvement, from product and process design through quality cost systems, audit programs, etc. (Evaluate) B. Documentation of the Quality System. Identify and apply quality system documentation components, including quality policies, procedures to support the system, configuration management and document control to manage work instructions, quality records, etc. (Apply) C. Quality Standards and Other Guidelines. Define and distinguish between national and international standards and other requirements and guidelines, including the Malcolm Baldrige National Quality Award (MBNQA), and describe key points of the ISO 9000 series of standards and how they are used. [Note: Industry-specific standards will not be tested.] (Apply) D. Quality Audits 1. Types of audits. Describe and distinguish between various types of quality audits such as product, process, management (system), registration (certification), compliance (regulatory), first, second, and third party, etc. (Apply)


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2. Roles and responsibilities in audits. Identify and define roles and responsibilities for audit participants such as audit team (leader and members), client, auditee, etc. (Understand) 3. Audit planning and implementation. Describe and apply the steps of a quality audit, from the audit planning stage through conducting the audit, from the perspective of an audit team member. (Apply) 4. Audit reporting and follow up. Identify, describe, and apply the steps of audit reporting and follow up, including the need to verify corrective action. (Apply) E. Cost of Quality (COQ). Identify and apply COQ concepts, including cost categories, data collection methods and classification, and reporting and interpreting results. (Analyze) F. Quality Training. Identify and define key elements of a training program, including conducting a needs analysis, developing curricula and materials, and determining the program’s effectiveness. (Apply) III. Product and Process Design (25 Questions) A. Classification of Quality Characteristics. Define, interpret, and classify quality characteristics for new products and processes. [Note: The classification of product defects is covered in IV.B.3.] (Evaluate) B. Design Inputs and Review. Identify sources of design inputs such as customer needs, regulatory requirements, etc. and how they translate into design concepts such as robust design, QFD, and Design for X (DFX, where X can mean six sigma (DFSS), manufacturability (DFM), cost (DFC), etc.). Identify and apply common elements of the design review process, including roles and responsibilities of participants. (Analyze) C. Technical Drawings and Specifications. Interpret technical drawings including characteristics such as views, title blocks, dimensioning, tolerancing, GD&T symbols, etc. Interpret specification requirements in relation to product and process characteristics. (Evaluate) D. Design Verification. Identify and apply various evaluations and tests to qualify and validate the design of new products and processes to ensure their fitness for use. (Evaluate) E. Reliability and Maintainability 1. Predictive and preventive maintenance tools. Describe and apply these tools and techniques to maintain and improve process and product reliability. (Analyze) 2. Reliability and maintainability indices. Review and analyze indices such as, MTTF, MTBF, MTTR, availability, failure rate, etc. (Analyze)



3. Bathtub curve. Identify, define, and distinguish between the basic elements of the bathtub curve. (Analyze) 4. Reliability/safety/hazard assessment tools. Define, construct, and interpret the results of failure mode and effects analysis (FMEA), failure mode, effects, and criticality analysis (FMECA), and fault tree analysis (FTA). (Analyze) IV. Product and Process Control (32 Questions) A. Tools. Define, identify, and apply product and process control methods such as developing control plans, identifying critical control points, developing and validating work instructions, etc. (Analyze) B. Material Control 1. Material identification, status, and traceability. Define and distinguish these concepts, and describe methods for applying them in various situations. [Note: Product recall procedures will not be tested.] (Analyze) 2. Material segregation. Describe material segregation and its importance, and evaluate appropriate methods for applying it in various situations. (Evaluate) 3. Classification of defects. Define, describe, and classify the seriousness of product and process defects. (Evaluate) 4. Material review board (MRB). Identify the purpose and function of an MRB, and make appropriate disposition decisions in various situations. (Analyze) C. Acceptance Sampling 1. Sampling concepts. Define, describe, and apply the concepts of producer and consumer risk and related terms, including operating characteristic (OC) curves, acceptable quality limit (AQL), lot tolerance percent defective (LTPD), average outgoing quality (AOQ), average outgoing quality limit (AOQL), etc. (Analyze) 2. Sampling standards and plans. Interpret and apply ANSI/ASQ Z1.4 and Z1.9 standards for attributes and variables sampling. Identify and distinguish between single, double, multiple, sequential, and continuous sampling methods. Identify the characteristics of DodgeRomig sampling tables and when they should be used. (Analyze) 3. Sample integrity. Identify the techniques for establishing and maintaining sample integrity. (Analyze) D. Measurement and Test 1. Measurement tools. Select and describe appropriate uses of inspection tools such as gage blocks, calipers, micrometers, optical comparators, etc. (Analyze)



2. Destructive and nondestructive tests. Distinguish between destructive and nondestructive measurement test methods and apply them appropriately. (Analyze) E. Metrology. Identify, describe, and apply metrology techniques such as calibration systems, traceability to calibration standards, measurement error and its sources, and control and maintenance of measurement standards and devices. (Analyze) F. Measurement System Analysis (MSA). Calculate, analyze, and interpret repeatability and reproducibility (Gage R&R) studies, measurement correlation, capability, bias, linearity, etc., including both conventional and control chart methods. (Evaluate) V. Continuous Improvement (30 Questions) A. Quality Control Tools. Select, construct, apply, and interpret tools such as 1) flowcharts, 2) Pareto charts, 3) cause and effect diagrams, 4) control charts, 5) check sheets, 6) scatter diagrams, and 7) histograms. (Analyze) B. Quality Management and Planning Tools. Select, construct, apply, and interpret tools such as 1) affinity diagrams, 2) tree diagrams, 3) process decision program charts (PDPC), 4) matrix diagrams, 5) interrelationship digraphs, 6) prioritization matrices, and 7) activity network diagrams. (Analyze) C. Continuous Improvement Techniques. Define, describe, and distinguish between various continuous improvement models: total quality management (TQM), kaizen, plan-do-check-act (PDCA), six sigma, theory of constraints (TOC), lean, etc. (Analyze) D. Corrective Action. Identify, describe, and apply elements of the corrective action process including problem identification, failure analysis, root cause analysis, problem correction, recurrence control, verification of effectiveness, etc. (Evaluate) E. Preventive Action. Identify, describe, and apply various preventive action tools such as error-proofing/poka-yoke, robust design, etc., and analyze their effectiveness. (Evaluate) VI. Quantitative Methods and Tools (43 Questions) A. Collecting and Summarizing Data 1. Types of data. Define, classify, and compare discrete (attributes) and continuous (variables) data. (Apply) 2. Measurement scales. Define, describe, and use nominal, ordinal, interval, and ratio scales. (Apply) 3. Data collection methods. Describe various methods for collecting data, including tally or check sheets, data coding, automatic gaging, etc., and identify their strengths and weaknesses. (Apply)



4. Data accuracy. Describe the characteristics or properties of data (e.g., source/resource issues, flexibility, versatility, etc.) and various types of data errors or poor quality such as low accuracy, inconsistency, interpretation of data values, and redundancy. Identify factors that can influence data accuracy, and apply techniques for error detection and correction. (Apply) 5. Descriptive statistics. Describe, calculate, and interpret measures of central tendency and dispersion (central limit theorem), and construct and interpret frequency distributions including simple, categorical, grouped, ungrouped, and cumulative. (Evaluate) 6. Graphical methods for depicting relationships. Construct, apply, and interpret diagrams and charts such as stem-and-leaf plots, box-and-whisker plots, etc. [Note: Run charts and scatter diagrams are covered in V.A.] (Analyze) 7. Graphical methods for depicting distributions. Construct, apply, and interpret diagrams such as normal probability plots, Weibull plots, etc. [Note: Histograms are covered in V.A.] (Analyze) B. Quantitative Concepts 1. Terminology. Define and apply quantitative terms, including population, parameter, sample, statistic, random sampling, expected value, etc. (Analyze) 2. Drawing statistical conclusions. Distinguish between numeric and analytical studies. Assess the validity of statistical conclusions by analyzing the assumptions used and the robustness of the technique used. (Evaluate) 3. Probability terms and concepts. Describe and apply concepts such as independence, mutually exclusive, multiplication rules, complementary probability, joint occurrence of events, etc. (Apply) C. Probability Distributions 1. Continuous distributions. Define and distinguish between these distributions: normal, uniform, bivariate normal, exponential, lognormal, Weibull, chi square, Student’s t, F, etc. (Analyze) 2. Discrete distributions. Define and distinguish between these distributions: binomial, Poisson, hypergeometric, multinomial, etc. (Analyze) D. Statistical Decision-Making 1. Point estimates and confidence intervals. Define, describe, and assess the efficiency and bias of estimators. Calculate and interpret standard error, tolerance intervals, and confidence intervals. (Evaluate)



2. Hypothesis testing. Define, interpret, and apply hypothesis tests for means, variances, and proportions. Apply and interpret the concepts of significance level, power, type I and type II errors. Define and distinguish between statistical and practical significance. (Evaluate) 3. Paired-comparison tests. Define and use paired-comparison (parametric) hypothesis tests, and interpret the results. (Apply) 4. Goodness-of-fit tests. Define and use chi square and other goodness-of-fit tests, and interpret the results. (Apply) 5. Analysis of variance (ANOVA). Define and use ANOVAs and interpret the results. (Analyze) 6. Contingency tables. Define, construct, and use contingency tables to evaluate statistical significance. (Analyze) E. Relationships Between Variables 1. Linear regression. Calculate the regression equation for simple regressions and least squares estimates. Construct and interpret hypothesis tests for regression statistics. Use regression models for estimation and prediction, and analyze the uncertainty in the estimate. [Note: Non-linear models and parameters will not be tested.] (Analyze) 2. Simple linear correlation. Calculate the correlation coefficient and its confidence interval, and construct and interpret a hypothesis test for correlation statistics. [Note: Serial correlation will not be tested.] (Analyze) 3. Time-series analysis. Define, describe, and use time-series analysis including moving average, and interpret time-series graphs to identify trends and seasonal or cyclical variation. (Analyze) F. Statistical Process Control (SPC) 1. Objectives and benefits. Identify and explain objectives and benefits of SPC such as assessing process performance. (Understand) 2. Common and special causes. Describe, identify, and distinguish between these types of causes. (Analyze) 3. Selection of variable. Identify and select characteristics for monitoring by control chart. (Analyze) 4. Rational subgrouping. Define and apply the principles of rational subgrouping. (Apply) 5. Control charts. Identify, select, construct, and use various control – – charts, including X − R, X − s, individuals and moving range (ImR or XmR), moving average and moving range (MamR), p, np, c, u, and CUSUM charts. (Analyze)



6. Control chart analysis. Read and interpret control charts, use rules for determining statistical control. (Evaluate) 7. Pre-control charts. Define and describe how these charts differ from other control charts and how they should be used. (Apply) 8. Short-run SPC. Identify, define, and use short-run SPC rules. (Apply) G. Process and Performance Capability 1. Process capability studies. Define, describe, calculate, and use process capability studies, including identifying characteristics, specifications, and tolerances, developing sampling plans for such studies, establishing statistical control, etc. (Analyze) 2. Process performance vs. specifications. Distinguish between natural process limits and specification limits, and calculate percent defective. (Analyze) 3. Process capability indices. Define, select, and calculate Cp, Cpk, Cpm, and Cr, and evaluate process capability. (Evaluate) 4. Process performance indices. Define, select, and calculate Pp and Ppk and evaluate process performance. (Evaluate) H. Design and Analysis of Experiments 1. Terminology. Define terms such as dependent and independent variables, factors, levels, response, treatment, error, and replication. (Understand) 2. Planning and organizing experiments. Define, describe, and apply the basic elements of designed experiments, including determining the experiment objective, selecting factors, responses, and measurement methods, choosing the appropriate design, etc. (Analyze) 3. Design principles. Define and apply the principles of power and sample size, balance, replication, order, efficiency, randomization, blocking, interaction, and confounding. (Apply) 4. One-factor experiments. Construct one-factor experiments such as completely randomized, randomized block, and Latin square designs, and use computational and graphical methods to analyze the significance of results. (Analyze) 5. Full-factorial experiments. Construct full-factorial designs and use computational and graphical methods to analyze the significance of results. (Analyze) 6. Two-level fractional factorial experiments. Construct two-level fractional factorial designs (including Taguchi designs) and apply computational and graphical methods to analyze the significance of results. (Analyze)



LEVELS OF COGNITION BASED ON BLOOM’S TAXONOMY—REVISED (2001) In addition to content specifics, the subtext for each topic in this BoK also indicates the intended complexity level of the test questions for that topic. These levels are based on “Levels of Cognition” (from Bloom’s Taxonomy—Revised, 2001) and are presented below in rank order, from least complex to most complex.

Remember Recall or recognize terms, definitions, facts, ideas, materials, patterns, sequences, methods, principles, etc.

Understand Read and understand descriptions, communications, reports, tables, diagrams, directions, regulations, etc.

Apply Know when and how to use ideas, procedures, methods, formulas, principles, theories, etc.

Analyze Break down information into its constituent parts and recognize their relationship to one another and how they are organized; identify sublevel factors or salient data from a complex scenario.

Evaluate Make judgments about the value of proposed ideas, solutions, etc., by comparing the proposal to specific criteria or standards.

Create Put parts or elements together in such a way as to reveal a pattern or structure not clearly there before; identify which data or information from a complex set is appropriate to examine further or from which supported conclusions can be drawn.



Appendix B Control Limit Formulas

VARIABLES CHARTS x and R chart:

Averages Chart : x ± A2 R Range Ch a rt : LCL = D3 R

x and s chart:

Averages C hart : x ± A3 s

UCL = D4 R

Std. Dev. Chart : LCL = B3 s UCL = B4 s

Individuals and Moving Range Chart (two-vaa lue moving window): Individuals Chart : x ± 2.. 66R Moving Range : UCL = 3 . 267 R Moving Average and Moving Range (two-value moving window)): Moving Average : x ± 1 . 88R

Moving Range : UCL = 3 . 267 R

ATTRIBUTES CHARTS p (1 − p )

p chart:

p±3

np chart:

np ± 3 np ( 1 − p )

c cha rt:

c ±3 c

u chart:

u±3

n

u n

585



Appendix C Constants for Control Charts

S u b g ro u p size n

A2

d2

D3

D4

A3

C4

B3

B4

E2

A2 for median charts

2

1.880

1.128

0

3.267

2.659

0.798

0

3.267

2.660

1.880

3

1.023

1.693

0

2.574

1.954

0.886

0

2.568

1.772

1.187

4

0.729

2.059

0

2.282

1.628

0.921

0

2.266

1.457

0.796

5

0.577

2.326

0

2.114

1.427

0.940

0

2.089

1.290

0.691

6

0.483

2.534

0

2.004

1.287

0.952

0.030

1.970

1.184

0.548

7

0.419

2.704

0.076

1.924

1.182

0.959

0.118

1.882

1.109

0.508

8

0.373

2.847

0.136

1.864

1.099

0.965

0.185

1.815

1.054

0.433

9

0.337

2.970

0.184

1.816

1.032

0.969

0.239

1.761

1.010

0.412

10

0.308

3.078

0.223

1.777

0.975

0.973

0.284

1.716

0.975

0.362

586



Appendix D Statistical Tolerance Factors for at Least 99 Percent of the Population (“k-Values”)

One-sided tolerance Confidence level

Two-sided tolerance Confidence level

n

0.90

0.95

0.99

n

0.90

0.95

0.99

10

3.532

3.981

5.075

10

3.959

4.433

5.594

11

3.444

3.852

4.828

11

3.849

4.277

5.308

12

3.371

3.747

4.633

12

3.758

4.150

5.079

13

3.310

3.659

4.472

13

3.682

4.044

4.893

14

3.257

3.585

4.336

14

3.618

3.955

4.737

15

3.212

3.520

4.224

15

3.562

3.878

4.605

16

3.172

3.463

4.124

16

3.514

3.812

4.492

17

3.136

3.415

4.038

17

3.471

3.754

4.393

18

3.106

3.370

3.961

18

3.433

3.702

4.307

19

3.078

3.331

3.893

19

3.399

3.656

4.230

20

3.052

3.295

3.832

20

3.368

3.615

4.161

21

3.028

3.262

3.776

21

3.340

3.577

4.100

22

3.007

3.233

3.727

22

3.315

3.543

4.044

23

2.987

3.206

3.680

23

3.292

3.512

3.993

24

2.969

3.181

3.638

24

3.270

3.483

3.947

25

2.952

3.158

3.601

25

3.251

3.457

3.904

30

2.884

3.064

3.446

30

3.170

3.350

3.733

40

2.793

2.941

3.250

40

3.066

3.213

3.518

50

2.735

2.863

3.124

50

3.001

3.126

3.385

587



Appendix E Standard Normal Distribution for Selected Z-Values

Z

Area to left of Z

Area to right of Z

Parts per million right of Z

0

0.5000000

0.5000000

500000.0002

0.1

0.5398279

0.4601721

460172.1045

0.2

0.5792597

0.4207403

420740.3128

0.3

0.6179114

0.3820886

382088.6425

0.4

0.6554217

0.3445783

344578.3034

0.5

0.6914625

0.3085375

308537.5326

0.6

0.7257469

0.2742531

274253.0649

0.7

0.7580364

0.2419636

241963.5785

0.8

0.7881447

0.2118553

211855.3339

0.9

0.8159399

0.1840601

184060.0917

1

0.8413447

0.1586553

158655.2598

1.1

0.8643339

0.1356661

135666.1015

1.2

0.8849303

0.1150697

115069.7317

1.3

0.9031995

0.0968005

96800.5495

1.4

0.9192433

0.0807567

80756.71126

1.5

0.9331928

0.0668072

66807.22879

1.6

0.9452007

0.0547993

54799.28945

1.7

0.9554346

0.0445654

44565.43178

1.8

0.9640697

0.0359303

35930.26551

1.9

0.9712835

0.0287165

28716.49286

2

0.9772499

0.0227501

22750.06204

2.1

0.9821356

0.0178644

17864.35742

2.2

0.9860966

0.0139034

13903.39891

2.3

0.9892759

0.0107241

10724.08106

2.4

0.9918025

8.1975289E-03

8197.528869

2.5

0.9937903

6.2096799E-03

6209.679859

2.6

0.9953388

4.6612218E-03

4661.221783

Continued

588


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Continued Z

Area to left of Z

Area to right of Z

Parts per million right of Z

2.7

0.9965330

3.4670231E-03

3467.023053

2.8

0.9974448

2.5551906E-03

2555.190642

2.9

0.9981341

1.8658801E-03

1865.88014

3

0.9986500

1.3499672E-03

1349.967223

3.1

0.9990323

9.6767124E-04

967.6712356

3.2

0.9993128

6.8720208E-04

687.2020808

3.3

0.9995165

4.8348254E-04

483.4825366

3.4

0.9996630

3.3698082E-04

336.9808229

3.5

0.9997673

2.3267337E-04

232.6733737

3.6

0.9998409

1.5914571E-04

159.1457138

3.7

0.9998922

1.0783015E-04

107.8301454

3.8

0.9999276

7.2372434E-05

72.37243427

3.9

0.9999519

4.8115519E-05

48.11551887

4

0.9999683

3.1686035E-05

31.68603461

4.1

0.9999793

2.0668716E-05

20.66871577

4.2

0.9999866

1.3354097E-05

13.35409733

4.3

0.9999915

8.5460212E-06

8.546021191

4.4

0.9999946

5.4169531E-06

5.416953054

4.5

0.9999966

3.4008031E-06

3.400803062

4.6

0.9999979

2.1146434E-06

2.114643376

4.7

0.9999987

1.3023157E-06

1.302315654

4.8

0.9999992

7.9435267E-07

0.794352669

4.9

0.9999995

4.7986955E-07

0.479869547

5

0.9999997

2.8710500E-07

0.287105

5.1

0.9999998

1.7012231E-07

0.170122314

5.2

0.9999999

9.9834400E-08

0.0998344

5.3

0.9999999

5.8022066E-08

0.058022066

5.4

1.0000000

3.3396123E-08

0.033396123

5.5

1.0000000

1.9036399E-08

0.019036399

5.6

1.0000000

1.0746217E-08

0.010746217

5.7

1.0000000

6.0076532E-09

0.006007653

5.8

1.0000000

3.3260517E-09

0.003326052

5.9

1.0000000

1.8235793E-09

0.001823579

6

1.0000000

9.9012187E-10

0.000990122



Appendix F Areas under Standard Normal Distribution to the Left of Z-Values

p(Z ≤ z)

z

0

z

–0.09

–0.08

–0.07

–0.06

–0.05

–0.04

–0.03

–0.02

–0.01

–0.00

–3.5

0.0002

0.0002

0.0002

0.0002

0.0002

0.0002

0.0002

0.0002

0.0002

0.0002

–3.4

0.0002

0.0003

0.0003

0.0003

0.0003

0.0003

0.0003

0.0003

0.0003

0.0003

–3.3

0.0003

0.0004

0.0004

0.0004

0.0004

0.0004

0.0004

0.0005

0.0005

0.0005

–3.2

0.0005

0.0005

0.0005

0.0006

0.0006

0.0006

0.0006

0.0006

0.0007

0.0007

–3.1

0.0007

0.0007

0.0008

0.0008

0.0008

0.0008

0.0009

0.0009

0.0009

0.0010

–3.0

0.0010

0.0010

0.0011

0.0011

0.0011

0.0012

0.0012

0.0013

0.0013

0.0013

–2.9

0.0014

0.0014

0.0015

0.0015

0.0016

0.0016

0.0017

0.0018

0.0018

0.0019

–2.8

0.0019

0.0020

0.0021

0.0021

0.0022

0.0023

0.0023

0.0024

0.0025

0.0026

–2.7

0.0026

0.0027

0.0028

0.0029

0.0030

0.0031

0.0032

0.0033

0.0034

0.0035

–2.6

0.0036

0.0037

0.0038

0.0039

0.0040

0.0041

0.0043

0.0044

0.0045

0.0047

–2.5

0.0048

0.0049

0.0051

0.0052

0.0054

0.0055

0.0057

0.0059

0.0060

0.0062

–2.4

0.0064

0.0066

0.0068

0.0069

0.0071

0.0073

0.0075

0.0078

0.0080

0.0082

–2.3

0.0084

0.0087

0.0089

0.0091

0.0094

0.0096

0.0099

0.0102

0.0104

0.0107

–2.2

0.0110

0.0113

0.0116

0.0119

0.0122

0.0125

0.0129

0.0132

0.0136

0.0139

–2.1

0.0143

0.0146

0.0150

0.0154

0.0158

0.0162

0.0166

0.0170

0.0174

0.0179

–2.0

0.0183

0.0188

0.0192

0.0197

0.0202

0.0207

0.0212

0.0217

0.0222

0.0228

–1.9

0.0233

0.0239

0.0244

0.0250

0.0256

0.0262

0.0268

0.0274

0.0281

0.0287

–1.8

0.0294

0.0301

0.0307

0.0314

0.0322

0.0329

0.0336

0.0344

0.0351

0.0359

–1.7

0.0367

0.0375

0.0384

0.0392

0.0401

0.0409

0.0418

0.0427

0.0436

0.0446

–1.6

0.0455

0.0465

0.0475

0.0485

0.0495

0.0505

0.0516

0.0526

0.0537

0.0548

–1.5

0.0559

0.0571

0.0582

0.0594

0.0606

0.0618

0.0630

0.0643

0.0655

0.0668 Continued

590


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Appendix F: Areas under Standard Normal Distribution to the Left of Z-Values 591

Continued z

–0.09

–0.08

–0.07

–0.06

–0.05

–0.04

–0.03

–0.02

–0.01

–0.00

–1.4

0.0681

0.0694

0.0708

0.0721

0.0735

0.0749

0.0764

0.0778

0.0793

0.0808

–1.3

0.0823

0.0838

0.0853

0.0869

0.0885

0.0901

0.0918

0.0934

0.0951

0.0968

–1.2

0.0985

0.1003

0.1020

0.1038

0.1056

0.1075

0.1093

0.1112

0.1131

0.1151

–1.1

0.1170

0.1190

0.1210

0.1230

0.1251

0.1271

0.1292

0.1314

0.1335

0.1357

–1.0

0.1379

0.1401

0.1423

0.1446

0.1469

0.1492

0.1515

0.1539

0.1562

0.1587

–0.9

0.1611

0.1635

0.1660

0.1685

0.1711

0.1736

0.1762

0.1788

0.1814

0.1841

–0.8

0.1867

0.1894

0.1922

0.1949

0.1977

0.2005

0.2033

0.2061

0.2090

0.2119

–0.7

0.2148

0.2177

0.2206

0.2236

0.2266

0.2296

0.2327

0.2358

0.2389

0.2420

–0.6

0.2451

0.2483

0.2514

0.2546

0.2578

0.2611

0.2643

0.2676

0.2709

0.2743

–0.5

0.2776

0.2810

0.2843

0.2877

0.2912

0.2946

0.2981

0.3015

0.3050

0.3085

–0.4

0.3121

0.3156

0.3192

0.3228

0.3264

0.3300

0.3336

0.3372

0.3409

0.3446

–0.3

0.3483

0.3520

0.3557

0.3594

0.3632

0.3669

0.3707

0.3745

0.3783

0.3821

–0.2

0.3859

0.3897

0.3936

0.3974

0.4013

0.4052

0.4090

0.4129

0.4168

0.4207

–0.1

0.4247

0.4286

0.4325

0.4364

0.4404

0.4443

0.4483

0.4522

0.4562

0.4602

0.0

0.4641

0.4681

0.4721

0.4761

0.4801

0.4840

0.4880

0.4920

0.4960

0.5000

p(Z ≤ z)

0

z

z

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.0

0.5000

0.5040

0.5080

0.5120

0.5160

0.5199

0.5239

0.5279

0.5319

0.5359

0.1

0.5398

0.5438

0.5478

0.5517

0.5557

0.5596

0.5636

0.5675

0.5714

0.5753

0.2

0.5793

0.5832

0.5871

0.5910

0.5948

0.5987

0.6026

0.6064

0.6103

0.6141

0.3

0.6179

0.6217

0.6255

0.6293

0.6331

0.6368

0.6406

0.6443

0.6480

0.6517

0.4

0.6554

0.6591

0.6628

0.6664

0.6700

0.6736

0.6772

0.6808

0.6844

0.6879

0.5

0.6915

0.6950

0.6985

0.7019

0.7054

0.7088

0.7123

0.7157

0.7190

0.7224

0.6

0.7257

0.7291

0.7324

0.7357

0.7389

0.7422

0.7454

0.7486

0.7517

0.7549

0.7

0.7580

0.7611

0.7642

0.7673

0.7704

0.7734

0.7764

0.7794

0.7823

0.7852

0.8

0.7881

0.7910

0.7939

0.7967

0.7995

0.8023

0.8051

0.8078

0.8106

0.8133

0.9

0.8159

0.8186

0.8212

0.8238

0.8264

0.8289

0.8315

0.8340

0.8365

0.8389

1.0

0.8413

0.8438

0.8461

0.8485

0.8508

0.8531

0.8554

0.8577

0.8599

0.8621 Continued



Continued z

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1.1

0.8643

0.8665

0.8686

0.8708

0.8729

0.8749

0.8770

0.8790

0.8810

0.8830

1.2

0.8849

0.8869

0.8888

0.8907

0.8925

0.8944

0.8962

0.8980

0.8997

0.9015

1.3

0.9032

0.9049

0.9066

0.9082

0.9099

0.9115

0.9131

0.9147

0.9162

0.9177

1.4

0.9192

0.9207

0.9222

0.9236

0.9251

0.9265

0.9279

0.9292

0.9306

0.9319

1.5

0.9332

0.9345

0.9357

0.9370

0.9382

0.9394

0.9406

0.9418

0.9429

0.9441

1.6

0.9452

0.9463

0.9474

0.9484

0.9495

0.9505

0.9515

0.9525

0.9535

0.9545

1.7

0.9554

0.9564

0.9573

0.9582

0.9591

0.9599

0.9608

0.9616

0.9625

0.9633

1.8

0.9641

0.9649

0.9656

0.9664

0.9671

0.9678

0.9686

0.9693

0.9699

0.9706

1.9

0.9713

0.9719

0.9726

0.9732

0.9738

0.9744

0.9750

0.9756

0.9761

0.9767

2.0

0.9772

0.9778

0.9783

0.9788

0.9793

0.9798

0.9803

0.9808

0.9812

0.9817

2.1

0.9821

0.9826

0.9830

0.9834

0.9838

0.9842

0.9846

0.9850

0.9854

0.9857

2.2

0.9861

0.9864

0.9868

0.9871

0.9875

0.9878

0.9881

0.9884

0.9887

0.9890

2.3

0.9893

0.9896

0.9898

0.9901

0.9904

0.9906

0.9909

0.9911

0.9913

0.9916

2.4

0.9918

0.9920

0.9922

0.9925

0.9927

0.9929

0.9931

0.9932

0.9934

0.9936

2.5

0.9938

0.9940

0.9941

0.9943

0.9945

0.9946

0.9948

0.9949

0.9951

0.9952

2.6

0.9953

0.9955

0.9956

0.9957

0.9959

0.9960

0.9961

0.9962

0.9963

0.9964

2.7

0.9965

0.9966

0.9967

0.9968

0.9969

0.9970

0.9971

0.9972

0.9973

0.9974

2.8

0.9974

0.9975

0.9976

0.9977

0.9977

0.9978

0.9979

0.9979

0.9980

0.9981

2.9

0.9981

0.9982

0.9982

0.9983

0.9984

0.9984

0.9985

0.9985

0.9986

0.9986

3.0

0.9987

0.9987

0.9987

0.9988

0.9988

0.9989

0.9989

0.9989

0.9990

0.9990

3.1

0.9990

0.9991

0.9991

0.9991

0.9992

0.9992

0.9992

0.9992

0.9993

0.9993

3.2

0.9993

0.9993

0.9994

0.9994

0.9994

0.9994

0.9994

0.9995

0.9995

0.9995

3.3

0.9995

0.9995

0.9995

0.9996

0.9996

0.9996

0.9996

0.9996

0.9996

0.9997

3.4

0.9997

0.9997

0.9997

0.9997

0.9997

0.9997

0.9997

0.9997

0.9997

0.9998

3.5

0.9998

0.9998

0.9998

0.9998

0.9998

0.9998

0.9998

0.9998

0.9998

0.9998



Appendix G F Distribution F0.10

593



F distribution F0.10

Denominator degrees of freedom

Numerator degrees of freedom 1

2

3

4

5

6

7

8

9

10

11

1

39.86

49.50

53.59

55.83

57.24

58.20

58.91

59.44

59.86

60.19

60.47

2

8.53

9.00

9.16

9.24

9.29

9.33

9.35

9.37

9.38

9.39

9.40

3

5.54

5.46

5.39

5.34

5.31

5.28

5.27

5.25

5.24

5.23

5.22

4

4.54

4.32

4.19

4.11

4.05

4.01

3.98

3.95

3.94

3.92

3.91

5

4.06

3.78

3.62

3.52

3.45

3.40

3.37

3.34

3.32

3.30

3.28

6

3.78

3.46

3.29

3.18

3.11

3.05

3.01

2.98

2.96

2.94

2.92

7

3.59

3.26

3.07

2.96

2.88

2.83

2.78

2.75

2.72

2.70

2.68

8

3.46

3.11

2.92

2.81

2.73

2.67

2.62

2.59

2.56

2.54

2.52

9

3.36

3.01

2.81

2.69

2.61

2.55

2.51

2.47

2.44

2.42

2.40

10

3.29

2.92

2.73

2.61

2.52

2.46

2.41

2.38

2.35

2.32

2.30

11

3.23

2.86

2.66

2.54

2.45

2.39

2.34

2.30

2.27

2.25

2.23

12

3.18

2.81

2.61

2.48

2.39

2.33

2.28

2.24

2.21

2.19

2.17

13

3.14

2.76

2.56

2.43

2.35

2.28

2.23

2.20

2.16

2.14

2.12

14

3.10

2.73

2.52

2.39

2.31

2.24

2.19

2.15

2.12

2.10

2.07

15

3.07

2.70

2.49

2.36

2.27

2.21

2.16

2.12

2.09

2.06

2.04

16

3.05

2.67

2.46

2.33

2.24

2.18

2.13

2.09

2.06

2.03

2.01

17

3.03

2.64

2.44

2.31

2.22

2.15

2.10

2.06

2.03

2.00

1.98

18

3.01

2.62

2.42

2.29

2.20

2.13

2.08

2.04

2.00

1.98

1.95

19

2.99

2.61

2.40

2.27

2.18

2.11

2.06

2.02

1.98

1.96

1.93

20

2.97

2.59

2.38

2.25

2.16

2.09

2.04

2.00

1.96

1.94

1.91

21

2.96

2.57

2.36

2.23

2.14

2.08

2.02

1.98

1.95

1.92

1.90

22

2.95

2.56

2.35

2.22

2.13

2.06

2.01

1.97

1.93

1.90

1.88

23

2.94

2.55

2.34

2.21

2.11

2.05

1.99

1.95

1.92

1.89

1.87

24

2.93

2.54

2.33

2.19

2.10

2.04

1.98

1.94

1.91

1.88

1.85

25

2.92

2.53

2.32

2.18

2.09

2.02

1.97

1.93

1.89

1.87

1.84

26

2.91

2.52

2.31

2.17

2.08

2.01

1.96

1.92

1.88

1.86

1.83

27

2.90

2.51

2.30

2.17

2.07

2.00

1.95

1.91

1.87

1.85

1.82

28

2.89

2.50

2.29

2.16

2.06

2.00

1.94

1.90

1.87

1.84

1.81

29

2.89

2.50

2.28

2.15

2.06

1.99

1.93

1.89

1.86

1.83

1.80

30

2.88

2.49

2.28

2.14

2.05

1.98

1.93

1.88

1.85

1.82

1.79

40

2.84

2.44

2.23

2.09

2.00

1.93

1.87

1.83

1.79

1.76

1.74

60

2.79

2.39

2.18

2.04

1.95

1.87

1.82

1.77

1.74

1.71

1.68

100

2.76

2.36

2.14

2.00

1.91

1.83

1.78

1.73

1.69

1.66

1.64

Continued


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Appendix G: F Distribution F0.10 595

F distribution F0.10 (continued)



13

14

15

16

17

18

19

20

21

22

1

60.71

60.90

61.07

61.22

61.35

61.46

61.57

61.66

61.74

61.81

61.88

2

9.41

9.41

9.42

9.42

9.43

9.43

9.44

9.44

9.44

9.44

9.45

3

5.22

5.21

5.20

5.20

5.20

5.19

5.19

5.19

5.18

5.18

5.18

4

3.90

3.89

3.88

3.87

3.86

3.86

3.85

3.85

3.84

3.84

3.84

5

3.27

3.26

3.25

3.24

3.23

3.22

3.22

3.21

3.21

3.20

3.20

6

2.90

2.89

2.88

2.87

2.86

2.85

2.85

2.84

2.84

2.83

2.83

7

2.67

2.65

2.64

2.63

2.62

2.61

2.61

2.60

2.59

2.59

2.58

8

2.50

2.49

2.48

2.46

2.45

2.45

2.44

2.43

2.42

2.42

2.41

9

2.38

2.36

2.35

2.34

2.33

2.32

2.31

2.30

2.30

2.29

2.29

10

2.28

2.27

2.26

2.24

2.23

2.22

2.22

2.21

2.20

2.19

2.19

11

2.21

2.19

2.18

2.17

2.16

2.15

2.14

2.13

2.12

2.12

2.11

12

2.15

2.13

2.12

2.10

2.09

2.08

2.08

2.07

2.06

2.05

2.05

13

2.10

2.08

2.07

2.05

2.04

2.03

2.02

2.01

2.01

2.00

1.99

14

2.05

2.04

2.02

2.01

2.00

1.99

1.98

1.97

1.96

1.96

1.95

15

2.02

2.00

1.99

1.97

1.96

1.95

1.94

1.93

1.92

1.92

1.91

16

1.99

1.97

1.95

1.94

1.93

1.92

1.91

1.90

1.89

1.88

1.88

17

1.96

1.94

1.93

1.91

1.90

1.89

1.88

1.87

1.86

1.86

1.85

18

1.93

1.92

1.90

1.89

1.87

1.86

1.85

1.84

1.84

1.83

1.82

19

1.91

1.89

1.88

1.86

1.85

1.84

1.83

1.82

1.81

1.81

1.80

20

1.89

1.87

1.86

1.84

1.83

1.82

1.81

1.80

1.79

1.79

1.78

21

1.87

1.86

1.84

1.83

1.81

1.80

1.79

1.78

1.78

1.77

1.76

22

1.86

1.84

1.83

1.81

1.80

1.79

1.78

1.77

1.76

1.75

1.74

23

1.84

1.83

1.81

1.80

1.78

1.77

1.76

1.75

1.74

1.74

1.73

24

1.83

1.81

1.80

1.78

1.77

1.76

1.75

1.74

1.73

1.72

1.71

25

1.82

1.80

1.79

1.77

1.76

1.75

1.74

1.73

1.72

1.71

1.70

26

1.81

1.79

1.77

1.76

1.75

1.73

1.72

1.71

1.71

1.70

1.69

27

1.80

1.78

1.76

1.75

1.74

1.72

1.71

1.70

1.70

1.69

1.68

28

1.79

1.77

1.75

1.74

1.73

1.71

1.70

1.69

1.69

1.68

1.67

29

1.78

1.76

1.75

1.73

1.72

1.71

1.69

1.68

1.68

1.67

1.66

30

1.77

1.75

1.74

1.72

1.71

1.70

1.69

1.68

1.67

1.66

1.65

40

1.71

1.70

1.68

1.66

1.65

1.64

1.62

1.61

1.61

1.60

1.59

60

1.66

1.64

1.62

1.60

1.59

1.58

1.56

1.55

1.54

1.53

1.53

100

1.61

1.59

1.57

1.56

1.54

1.53

1.52

1.50

1.49

1.48

1.48

Continued






24

25

26

27

28

29

30

40

60

100

1

61.94

62.00

62.05

62.10

62.15

62.19

62.23

62.26

62.53

62.79

63.01

2

9.45

9.45

9.45

9.45

9.45

9.46

9.46

9.46

9.47

9.47

9.48

3

5.18

5.18

5.17

5.17

5.17

5.17

5.17

5.17

5.16

5.15

5.14

4

3.83

3.83

3.83

3.83

3.82

3.82

3.82

3.82

3.80

3.79

3.78

5

3.19

3.19

3.19

3.18

3.18

3.18

3.18

3.17

3.16

3.14

3.13

6

2.82

2.82

2.81

2.81

2.81

2.81

2.80

2.80

2.78

2.76

2.75

7

2.58

2.58

2.57

2.57

2.56

2.56

2.56

2.56

2.54

2.51

2.50

8

2.41

2.40

2.40

2.40

2.39

2.39

2.39

2.38

2.36

2.34

2.32

9

2.28

2.28

2.27

2.27

2.26

2.26

2.26

2.25

2.23

2.21

2.19

10

2.18

2.18

2.17

2.17

2.17

2.16

2.16

2.16

2.13

2.11

2.09

11

2.11

2.10

2.10

2.09

2.09

2.08

2.08

2.08

2.05

2.03

2.01

12

2.04

2.04

2.03

2.03

2.02

2.02

2.01

2.01

1.99

1.96

1.94

13

1.99

1.98

1.98

1.97

1.97

1.96

1.96

1.96

1.93

1.90

1.88

14

1.94

1.94

1.93

1.93

1.92

1.92

1.92

1.91

1.89

1.86

1.83

15

1.90

1.90

1.89

1.89

1.88

1.88

1.88

1.87

1.85

1.82

1.79

16

1.87

1.87

1.86

1.86

1.85

1.85

1.84

1.84

1.81

1.78

1.76

17

1.84

1.84

1.83

1.83

1.82

1.82

1.81

1.81

1.78

1.75

1.73

18

1.82

1.81

1.80

1.80

1.80

1.79

1.79

1.78

1.75

1.72

1.70

19

1.79

1.79

1.78

1.78

1.77

1.77

1.76

1.76

1.73

1.70

1.67

20

1.77

1.77

1.76

1.76

1.75

1.75

1.74

1.74

1.71

1.68

1.65

21

1.75

1.75

1.74

1.74

1.73

1.73

1.72

1.72

1.69

1.66

1.63

22

1.74

1.73

1.73

1.72

1.72

1.71

1.71

1.70

1.67

1.64

1.61

23

1.72

1.72

1.71

1.70

1.70

1.69

1.69

1.69

1.66

1.62

1.59

24

1.71

1.70

1.70

1.69

1.69

1.68

1.68

1.67

1.64

1.61

1.58

25

1.70

1.69

1.68

1.68

1.67

1.67

1.66

1.66

1.63

1.59

1.56

26

1.68

1.68

1.67

1.67

1.66

1.66

1.65

1.65

1.61

1.58

1.55

27

1.67

1.67

1.66

1.65

1.65

1.64

1.64

1.64

1.60

1.57

1.54

28

1.66

1.66

1.65

1.64

1.64

1.63

1.63

1.63

1.59

1.56

1.53

29

1.65

1.65

1.64

1.63

1.63

1.62

1.62

1.62

1.58

1.55

1.52

30

1.64

1.64

1.63

1.63

1.62

1.62

1.61

1.61

1.57

1.54

1.51

40

1.58

1.57

1.57

1.56

1.56

1.55

1.55

1.54

1.51

1.47

1.43

60

1.52

1.51

1.50

1.50

1.49

1.49

1.48

1.48

1.44

1.40

1.36

100

1.47

1.46

1.45

1.45

1.44

1.43

1.43

1.42

1.38

1.34

1.29



Appendix H F Distribution F0.05

597






2

3

4

5

6

7

8

9

10

11

1

161.4

199.5

215.7

224.6

230.2

234.0

236.8

238.9

240.5

241.9

243.0

2

18.51

19.00

19.16

19.25

19.30

19.33

19.35

19.37

19.38

19.40

19.40

3

10.13

9.55

9.28

9.12

9.01

8.94

8.89

8.85

8.81

8.79

8.76

4

7.71

6.94

6.59

6.39

6.26

6.16

6.09

6.04

6.00

5.96

5.94

5

6.61

5.79

5.41

5.19

5.05

4.95

4.88

4.82

4.77

4.74

4.70

6

5.99

5.14

4.76

4.53

4.39

4.28

4.21

4.15

4.10

4.06

4.03

7

5.59

4.74

4.35

4.12

3.97

3.87

3.79

3.73

3.68

3.64

3.60

8

5.32

4.46

4.07

3.84

3.69

3.58

3.50

3.44

3.39

3.35

3.31

9

5.12

4.26

3.86

3.63

3.48

3.37

3.29

3.23

3.18

3.14

3.10

10

4.96

4.10

3.71

3.48

3.33

3.22

3.14

3.07

3.02

2.98

2.94

11

4.84

3.98

3.59

3.36

3.20

3.09

3.01

2.95

2.90

2.85

2.82

12

4.75

3.89

3.49

3.26

3.11

3.00

2.91

2.85

2.80

2.75

2.72

13

4.67

3.81

3.41

3.18

3.03

2.92

2.83

2.77

2.71

2.67

2.63

14

4.60

3.74

3.34

3.11

2.96

2.85

2.76

2.70

2.65

2.60

2.57

15

4.54

3.68

3.29

3.06

2.90

2.79

2.71

2.64

2.59

2.54

2.51

16

4.49

3.63

3.24

3.01

2.85

2.74

2.66

2.59

2.54

2.49

2.46

17

4.45

3.59

3.20

2.96

2.81

2.70

2.61

2.55

2.49

2.45

2.41

18

4.41

3.55

3.16

2.93

2.77

2.66

2.58

2.51

2.46

2.41

2.37

19

4.38

3.52

3.13

2.90

2.74

2.63

2.54

2.48

2.42

2.38

2.34

20

4.35

3.49

3.10

2.87

2.71

2.60

2.51

2.45

2.39

2.35

2.31

21

4.32

3.47

3.07

2.84

2.68

2.57

2.49

2.42

2.37

2.32

2.28

22

4.30

3.44

3.05

2.82

2.66

2.55

2.46

2.40

2.34

2.30

2.26

23

4.28

3.42

3.03

2.80

2.64

2.53

2.44

2.37

2.32

2.27

2.24

24

4.26

3.40

3.01

2.78

2.62

2.51

2.42

2.36

2.30

2.25

2.22

25

4.24

3.39

2.99

2.76

2.60

2.49

2.40

2.34

2.28

2.24

2.20

26

4.23

3.37

2.98

2.74

2.59

2.47

2.39

2.32

2.27

2.22

2.18

27

4.21

3.35

2.96

2.73

2.57

2.46

2.37

2.31

2.25

2.20

2.17

28

4.20

3.34

2.95

2.71

2.56

2.45

2.36

2.29

2.24

2.19

2.15

29

4.18

3.33

2.93

2.70

2.55

2.43

2.35

2.28

2.22

2.18

2.14

30

4.17

3.32

2.92

2.69

2.53

2.42

2.33

2.27

2.21

2.16

2.13

40

4.08

3.23

2.84

2.61

2.45

2.34

2.25

2.18

2.12

2.08

2.04

60

4.00

3.15

2.76

2.53

2.37

2.25

2.17

2.10

2.04

1.99

1.95

100

3.94

3.09

2.70

2.46

2.31

2.19

2.10

2.03

1.97

1.93

1.89

Continued


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Appendix H: F Distribution F0.05 599




13

14

15

16

17

18

19

20

21

22

1

243.9

244.7

245.4

245.9

246.5

246.9

247.3

247.7

248.0

248.3

248.6

2

19.41

19.42

19.42

19.43

19.43

19.44

19.44

19.44

19.45

19.45

19.45

3

8.74

8.73

8.71

8.70

8.69

8.68

8.67

8.67

8.66

8.65

8.65

4

5.91

5.89

5.87

5.86

5.84

5.83

5.82

5.81

5.80

5.79

5.79

5

4.68

4.66

4.64

4.62

4.60

4.59

4.58

4.57

4.56

4.55

4.54

6

4.00

3.98

3.96

3.94

3.92

3.91

3.90

3.88

3.87

3.86

3.86

7

3.57

3.55

3.53

3.51

3.49

3.48

3.47

3.46

3.44

3.43

3.43

8

3.28

3.26

3.24

3.22

3.20

3.19

3.17

3.16

3.15

3.14

3.13

9

3.07

3.05

3.03

3.01

2.99

2.97

2.96

2.95

2.94

2.93

2.92

10

2.91

2.89

2.86

2.85

2.83

2.81

2.80

2.79

2.77

2.76

2.75

11

2.79

2.76

2.74

2.72

2.70

2.69

2.67

2.66

2.65

2.64

2.63

12

2.69

2.66

2.64

2.62

2.60

2.58

2.57

2.56

2.54

2.53

2.52

13

2.60

2.58

2.55

2.53

2.51

2.50

2.48

2.47

2.46

2.45

2.44

14

2.53

2.51

2.48

2.46

2.44

2.43

2.41

2.40

2.39

2.38

2.37

15

2.48

2.45

2.42

2.40

2.38

2.37

2.35

2.34

2.33

2.32

2.31

16

2.42

2.40

2.37

2.35

2.33

2.32

2.30

2.29

2.28

2.26

2.25

17

2.38

2.35

2.33

2.31

2.29

2.27

2.26

2.24

2.23

2.22

2.21

18

2.34

2.31

2.29

2.27

2.25

2.23

2.22

2.20

2.19

2.18

2.17

19

2.31

2.28

2.26

2.23

2.21

2.20

2.18

2.17

2.16

2.14

2.13

20

2.28

2.25

2.22

2.20

2.18

2.17

2.15

2.14

2.12

2.11

2.10

21

2.25

2.22

2.20

2.18

2.16

2.14

2.12

2.11

2.10

2.08

2.07

22

2.23

2.20

2.17

2.15

2.13

2.11

2.10

2.08

2.07

2.06

2.05

23

2.20

2.18

2.15

2.13

2.11

2.09

2.08

2.06

2.05

2.04

2.02

24

2.18

2.15

2.13

2.11

2.09

2.07

2.05

2.04

2.03

2.01

2.00

25

2.16

2.14

2.11

2.09

2.07

2.05

2.04

2.02

2.01

2.00

1.98

26

2.15

2.12

2.09

2.07

2.05

2.03

2.02

2.00

1.99

1.98

1.97

27

2.13

2.10

2.08

2.06

2.04

2.02

2.00

1.99

1.97

1.96

1.95

28

2.12

2.09

2.06

2.04

2.02

2.00

1.99

1.97

1.96

1.95

1.93

29

2.10

2.08

2.05

2.03

2.01

1.99

1.97

1.96

1.94

1.93

1.92

30

2.09

2.06

2.04

2.01

1.99

1.98

1.96

1.95

1.93

1.92

1.91

40

2.00

1.97

1.95

1.92

1.90

1.89

1.87

1.85

1.84

1.83

1.81

60

1.92

1.89

1.86

1.84

1.82

1.80

1.78

1.76

1.75

1.73

1.72

100

1.85

1.82

1.79

1.77

1.75

1.73

1.71

1.69

1.68

1.66

1.65

Continued






24

25

26

27

28

29

30

40

60

100

1

248.8

249.1

249.3

249.5

249.6

249.8

250.0

250.1

251.1

252.2

253.0

2

19.45

19.45

19.46

19.46

19.46

19.46

19.46

19.46

19.47

19.48

19.49

3

8.64

8.64

8.63

8.63

8.63

8.62

8.62

8.62

8.59

8.57

8.55

4

5.78

5.77

5.77

5.76

5.76

5.75

5.75

5.75

5.72

5.69

5.66

5

4.53

4.53

4.52

4.52

4.51

4.50

4.50

4.50

4.46

4.43

4.41

6

3.85

3.84

3.83

3.83

3.82

3.82

3.81

3.81

3.77

3.74

3.71

7

3.42

3.41

3.40

3.40

3.39

3.39

3.38

3.38

3.34

3.30

3.27

8

3.12

3.12

3.11

3.10

3.10

3.09

3.08

3.08

3.04

3.01

2.97

9

2.91

2.90

2.89

2.89

2.88

2.87

2.87

2.86

2.83

2.79

2.76

10

2.75

2.74

2.73

2.72

2.72

2.71

2.70

2.70

2.66

2.62

2.59

11

2.62

2.61

2.60

2.59

2.59

2.58

2.58

2.57

2.53

2.49

2.46

12

2.51

2.51

2.50

2.49

2.48

2.48

2.47

2.47

2.43

2.38

2.35

13

2.43

2.42

2.41

2.41

2.40

2.39

2.39

2.38

2.34

2.30

2.26

14

2.36

2.35

2.34

2.33

2.33

2.32

2.31

2.31

2.27

2.22

2.19

15

2.30

2.29

2.28

2.27

2.27

2.26

2.25

2.25

2.20

2.16

2.12

16

2.24

2.24

2.23

2.22

2.21

2.21

2.20

2.19

2.15

2.11

2.07

17

2.20

2.19

2.18

2.17

2.17

2.16

2.15

2.15

2.10

2.06

2.02

18

2.16

2.15

2.14

2.13

2.13

2.12

2.11

2.11

2.06

2.02

1.98

19

2.12

2.11

2.11

2.10

2.09

2.08

2.08

2.07

2.03

1.98

1.94

20

2.09

2.08

2.07

2.07

2.06

2.05

2.05

2.04

1.99

1.95

1.91

21

2.06

2.05

2.05

2.04

2.03

2.02

2.02

2.01

1.96

1.92

1.88

22

2.04

2.03

2.02

2.01

2.00

2.00

1.99

1.98

1.94

1.89

1.85

23

2.01

2.01

2.00

1.99

1.98

1.97

1.97

1.96

1.91

1.86

1.82

24

1.99

1.98

1.97

1.97

1.96

1.95

1.95

1.94

1.89

1.84

1.80

25

1.97

1.96

1.96

1.95

1.94

1.93

1.93

1.92

1.87

1.82

1.78

26

1.96

1.95

1.94

1.93

1.92

1.91

1.91

1.90

1.85

1.80

1.76

27

1.94

1.93

1.92

1.91

1.90

1.90

1.89

1.88

1.84

1.79

1.74

28

1.92

1.91

1.91

1.90

1.89

1.88

1.88

1.87

1.82

1.77

1.73

29

1.91

1.90

1.89

1.88

1.88

1.87

1.86

1.85

1.81

1.75

1.71

30

1.90

1.89

1.88

1.87

1.86

1.85

1.85

1.84

1.79

1.74

1.70

40

1.80

1.79

1.78

1.77

1.77

1.76

1.75

1.74

1.69

1.64

1.59

60

1.71

1.70

1.69

1.68

1.67

1.66

1.66

1.65

1.59

1.53

1.48

100

1.64

1.63

1.62

1.61

1.60

1.59

1.58

1.57

1.52

1.45

1.39



Appendix I F Distribution F0.01

601






2

3

4

5

6

7

8

9

10

11

1

4052

4999

5404

5624

5764

5859

5928

5981

6022

6056

6083

2

98.5

99

99.16

99.25

99.3

99.33

99.36

99.38

99.39

99.4

99.41

3

34.12

30.82

29.46

28.71

28.24

27.91

27.67

27.49

27.34

27.23

27.13

4

21.2

18

16.69

15.98

15.52

15.21

14.98

14.8

14.66

14.55

14.45

5

16.26

13.27

12.06

11.39

10.97

10.67

10.46

10.29

10.16

10.05

9.963

6

13.75

10.92

9.78

9.148

8.746

8.466

8.26

8.102

7.976

7.874

7.79

7

12.25

9.547

8.451

7.847

7.46

7.191

6.993

6.84

6.719

6.62

6.538

8

11.26

8.649

7.591

7.006

6.632

6.371

6.178

6.029

5.911

5.814

5.734

9

10.56

8.022

6.992

6.422

6.057

5.802

5.613

5.467

5.351

5.257

5.178

10

10.04

7.559

6.552

5.994

5.636

5.386

5.2

5.057

4.942

4.849

4.772

11

9.646

7.206

6.217

5.668

5.316

5.069

4.886

4.744

4.632

4.539

4.462

12

9.33

6.927

5.953

5.412

5.064

4.821

4.64

4.499

4.388

4.296

4.22

13

9.074

6.701

5.739

5.205

4.862

4.62

4.441

4.302

4.191

4.1

4.025

14

8.862

6.515

5.564

5.035

4.695

4.456

4.278

4.14

4.03

3.939

3.864

15

8.683

6.359

5.417

4.893

4.556

4.318

4.142

4.004

3.895

3.805

3.73

16

8.531

6.226

5.292

4.773

4.437

4.202

4.026

3.89

3.78

3.691

3.616

17

8.4

6.112

5.185

4.669

4.336

4.101

3.927

3.791

3.682

3.593

3.518

18

8.285

6.013

5.092

4.579

4.248

4.015

3.841

3.705

3.597

3.508

3.434

19

8.185

5.926

5.01

4.5

4.171

3.939

3.765

3.631

3.523

3.434

3.36

20

8.096

5.849

4.938

4.431

4.103

3.871

3.699

3.564

3.457

3.368

3.294

21

8.017

5.78

4.874

4.369

4.042

3.812

3.64

3.506

3.398

3.31

3.236

22

7.945

5.719

4.817

4.313

3.988

3.758

3.587

3.453

3.346

3.258

3.184

23

7.881

5.664

4.765

4.264

3.939

3.71

3.539

3.406

3.299

3.211

3.137

24

7.823

5.614

4.718

4.218

3.895

3.667

3.496

3.363

3.256

3.168

3.094

25

7.77

5.568

4.675

4.177

3.855

3.627

3.457

3.324

3.217

3.129

3.056

26

7.721

5.526

4.637

4.14

3.818

3.591

3.421

3.288

3.182

3.094

3.021

27

7.677

5.488

4.601

4.106

3.785

3.558

3.388

3.256

3.149

3.062

2.988

28

7.636

5.453

4.568

4.074

3.754

3.528

3.358

3.226

3.12

3.032

2.959

29

7.598

5.42

4.538

4.045

3.725

3.499

3.33

3.198

3.092

3.005

2.931

30

7.562

5.39

4.51

4.018

3.699

3.473

3.305

3.173

3.067

2.979

2.906

40

7.314

5.178

4.313

3.828

3.514

3.291

3.124

2.993

2.888

2.801

2.727

60

7.077

4.977

4.126

3.649

3.339

3.119

2.953

2.823

2.718

2.632

2.559

100 6.895

4.824

3.984

3.513

3.206

2.988

2.823

2.694

2.59

2.503

2.43

Continued


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Appendix I: F Distribution F0.01 603

F distribution F0.01 (continued) Numerator degrees of freedom


12

13

14

15

16

17

18

19

20

21

22

1

6107

6126

6143

6157

6170

6181

6191

6201

6208.7 6216.1 6223.1

2

99.42

99.42

99.43

99.43

99.44

99.44

99.44

99.45

99.448 99.451 99.455

3

27.05

26.98

26.92

26.87

26.83

26.79

26.75

26.72

4

14.37

14.31

14.25

14.2

14.15

14.11

14.08

14.05

5

9.888

9.825

9.77

9.722

9.68

9.643

9.609

9.58

9.5527 9.5281 9.5058

6

7.718

7.657

7.605

7.559

7.519

7.483

7.451

7.422

7.3958 7.3721 7.3506

7

6.469

6.41

6.359

6.314

6.275

6.24

6.209

6.181

6.1555 6.1324 6.1113

8

5.667

5.609

5.559

5.515

5.477

5.442

5.412

5.384

5.3591 5.3365 5.3157

26.69 26.664 26.639 14.019 13.994

13.97

9

5.111

5.055

5.005

4.962

4.924

4.89

4.86

4.833

4.808 4.7855 4.7651

10

4.706

4.65

4.601

4.558

4.52

4.487

4.457

4.43

4.4054 4.3831 4.3628

11

4.397

4.342

4.293

4.251

4.213

4.18

4.15

4.123

4.099 4.0769 4.0566

12

4.155

4.1

4.052

4.01

3.972

3.939

3.91

3.883

3.8584 3.8363 3.8161

13

3.96

3.905

3.857

3.815

3.778

3.745

3.716

3.689

3.6646 3.6425 3.6223

14

3.8

3.745

3.698

3.656

3.619

3.586

3.556

3.529

3.5052 3.4832

15

3.666

3.612

3.564

3.522

3.485

3.452

3.423

3.396

3.3719 3.3498 3.3297

16

3.553

3.498

3.451

3.409

3.372

3.339

3.31

3.283

3.2587 3.2367 3.2165

17

3.455

3.401

3.353

3.312

3.275

3.242

3.212

3.186

3.1615 3.1394 3.1192

18

3.371

3.316

3.269

3.227

3.19

3.158

3.128

3.101

3.0771

3.055 3.0348

19

3.297

3.242

3.195

3.153

3.116

3.084

3.054

3.027

3.0031

2.981 2.9607

20

3.231

3.177

3.13

3.088

3.051

3.018

2.989

2.962

2.9377 2.9156 2.8953

21

3.173

3.119

3.072

3.03

2.993

2.96

2.931

2.904

2.8795 2.8574

22

3.121

3.067

3.019

2.978

2.941

2.908

2.879

2.852

2.8274 2.8052 2.7849

23

3.074

3.02

2.973

2.931

2.894

2.861

2.832

2.805

2.7805 2.7582 2.7378

24

3.032

2.977

2.93

2.889

2.852

2.819

2.789

2.762

2.738 2.7157 2.6953

25

2.993

2.939

2.892

2.85

2.813

2.78

2.751

2.724

26

2.958

2.904

2.857

2.815

2.778

2.745

2.715

2.688

27

2.926

2.872

2.824

2.783

2.746

2.713

2.683

2.656

2.6316

28

2.896

2.842

2.795

2.753

2.716

2.683

2.653

2.626

2.6018 2.5793 2.5587

29

2.868

2.814

2.767

2.726

2.689

2.656

2.626

2.599

2.5742 2.5517 2.5311

30

2.843

2.789

2.742

2.7

2.663

2.63

2.6

2.573

2.5487 2.5262 2.5055

40

2.665

2.611

2.563

2.522

2.484

2.451

2.421

2.394

2.3689 2.3461 2.3252

60

2.496

2.442

2.394

2.352

2.315

2.281

2.251

2.223

2.1978 2.1747 2.1533

10

2.368

2.313

2.265

2.223

2.185

2.151

2.12

2.092

2.0666 2.0431 2.0214

2.6993

3.463

2.837

2.677 2.6565

2.664 2.6416 2.6211 2.609 2.5886

Continued



F distribution F0.01 (continued) Numerator degrees of freedom 23

24

25

26

27

29

30

40

60

100

1

6228.7 6234.3 6239.9 6244.5 6249.2 6252.9 6257.1 6260.4 6286.4

2

99.455 99.455 99.459 99.462 99.462 99.462 99.462 99.466 99.477 99.484 99.491

3

26.617 26.597 26.579 26.562 26.546 26.531 26.517 26.504 26.411 26.316 26.241

4

13.949 13.929 13.911 13.894 13.878 13.864

5

9.4853 9.4665 9.4492 9.4331 9.4183 9.4044 9.3914 9.3794 9.2912

6

7.3309 7.3128

7

6313 6333.9

13.85 13.838 13.745 13.652 13.577 9.202

9.13

7.296 7.2805 7.2661 7.2528 7.2403 7.2286 7.1432 7.0568 6.9867

6.092 6.0743 6.0579 6.0428 6.0287 6.0156 6.0035

5.992 5.9084 5.8236 5.7546

8

5.2967 5.2793 5.2631 5.2482 5.2344 5.2214 5.2094 5.1981 5.1156 5.0316 4.9633

9

4.7463

4.729

4.713 4.6982 4.6845 4.6717 4.6598 4.6486 4.5667 4.4831

10 4.3441 4.3269 4.3111 4.2963 4.2827 11 Denominator degrees of freedom

28

4.415

4.27 4.2582 4.2469 4.1653 4.0819 4.0137

4.038 4.0209 4.0051 3.9904 3.9768 3.9641 3.9522 3.9411 3.8596 3.7761 3.7077

12 3.7976 3.7805 3.7647 3.7501 3.7364 3.7238 3.7119 3.7008 3.6192 3.5355 3.4668 13 3.6038 3.5868

3.571 3.5563 3.5427

3.53 3.5182

3.507 3.4253 3.3413 3.2723

14 3.4445 3.4274 3.4116 3.3969 3.3833 3.3706 3.3587 3.3476 3.2657 3.1813 3.1118 15 3.3111

3.294 3.2782 3.2636 3.2499 3.2372 3.2253 3.2141 3.1319 3.0471 2.9772

16 3.1979 3.1808

3.165 3.1503 3.1366 3.1238 3.1119 3.1007 3.0182

2.933 2.8627

17 3.1006 3.0835 3.0676 3.0529 3.0392 3.0264 3.0145 3.0032 2.9204 2.8348 2.7639 18 3.0161

2.999 2.9831 2.9683 2.9546 2.9418 2.9298 2.9185 2.8354 2.7493 2.6779

19 2.9421 2.9249 2.9089 2.8942 2.8804 2.8675 2.8555 2.8442 2.7608 2.6742 2.6023 20 2.8766 2.8594 2.8434 2.8286 2.8148 2.8019 2.7898 2.7785 2.6947 2.6077 2.5353 21 2.8183

2.801

2.785 2.7702 2.7563 2.7434 2.7313

22 2.7661 2.7488 2.7328 2.7179

2.704

2.72 2.6359 2.5484 2.4755

2.691 2.6789 2.6675 2.5831 2.4951 2.4218

23 2.7191 2.7017 2.6857 2.6707 2.6568 2.6438 2.6316 2.6202 2.5355 2.4471 2.3732 24 2.6764 2.6591

2.643

2.628

2.614

25 2.6377 2.6203 2.6041 2.5891 2.5751

2.601 2.5888 2.5773 2.4923 2.4035 2.3291 2.562 2.5498 2.5383

2.453 2.3637 2.2888

26 2.6022 2.5848 2.5686 2.5535 2.5395 2.5264 2.5142 2.5026

2.417 2.3273 2.2519

27 2.5697 2.5522

2.536 2.5209 2.5069 2.4937 2.4814 2.4699

2.384 2.2938

28 2.5398 2.5223

2.506 2.4909 2.4768 2.4636 2.4513 2.4397 2.3535 2.2629 2.1867

29 2.5121 2.4946 2.4783 2.4631

2.449 2.4358 2.4234 2.4118 2.3253 2.2344 2.1577

30 2.4865 2.4689 2.4526 2.4374 2.4233 40 2.3059

2.218

2.288 2.2714 2.2559 2.2415

2.41 2.3976

2.386 2.2992 2.2079 2.1307

2.228 2.2153 2.2034 2.1142 2.0194 1.9383

60 2.1336 2.1154 2.0984 2.0825 2.0677 2.0538 2.0408 2.0285

1.936 1.8363 1.7493

100 2.0012 1.9826 1.9651 1.9489 1.9337 1.9194 1.9059 1.8933 1.7972 1.6918 1.5977



Appendix J Binomial Distribution Probability of x or fewer occurrences in a sample of size n

Binomial distribution p

n

x

2

0 0.980 0.960 0.941 0.922 0.903 0.884 0.865 0.846 0.828 0.810 0.723 0.640 0.563 0.490 0.423 0.360 0.303 0.250

2

1 1.000 1.000 0.999 0.998 0.998 0.996 0.995 0.994 0.992 0.990 0.978 0.960 0.938 0.910 0.878 0.840 0.798 0.750

3

0 0.970 0.941 0.913 0.885 0.857 0.831 0.804 0.779 0.754 0.729 0.614 0.512 0.422 0.343 0.275 0.216 0.166 0.125

3

1 1.000 0.999 0.997 0.995 0.993 0.990 0.986 0.982 0.977 0.972 0.939 0.896 0.844 0.784 0.718 0.648 0.575 0.500

3

2 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.999 0.997 0.992 0.984 0.973 0.957 0.936 0.909 0.875

4

0 0.961 0.922 0.885 0.849 0.815 0.781 0.748 0.716 0.686 0.656 0.522 0.410 0.316 0.240 0.179 0.130 0.092 0.063

4

1 0.999 0.998 0.995 0.991 0.986 0.980 0.973 0.966 0.957 0.948 0.890 0.819 0.738 0.652 0.563 0.475 0.391 0.313

4

2 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.998 0.997 0.996 0.988 0.973 0.949 0.916 0.874 0.821 0.759 0.688

4

3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998 0.996 0.992 0.985 0.974 0.959 0.938

5

0 0.951 0.904 0.859 0.815 0.774 0.734 0.696 0.659 0.624 0.590 0.444 0.328 0.237 0.168 0.116 0.078 0.050 0.031

5

1 0.999 0.996 0.992 0.985 0.977 0.968 0.958 0.946 0.933 0.919 0.835 0.737 0.633 0.528 0.428 0.337 0.256 0.188

5

2 1.000 1.000 1.000 0.999 0.999 0.998 0.997 0.995 0.994 0.991 0.973 0.942 0.896 0.837 0.765 0.683 0.593 0.500

5

3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.993 0.984 0.969 0.946 0.913 0.869 0.813

5

4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998 0.995 0.990 0.982 0.969

6

0 0.941 0.886 0.833 0.783 0.735 0.690 0.647 0.606 0.568 0.531 0.377 0.262 0.178 0.118 0.075 0.047 0.028 0.016

6

1 0.999 0.994 0.988 0.978 0.967 0.954 0.939 0.923 0.905 0.886 0.776 0.655 0.534 0.420 0.319 0.233 0.164 0.109

6

2 1.000 1.000 0.999 0.999 0.998 0.996 0.994 0.991 0.988 0.984 0.953 0.901 0.831 0.744 0.647 0.544 0.442 0.344

6

3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.999 0.994 0.983 0.962 0.930 0.883 0.821 0.745 0.656

6

4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.995 0.989 0.978 0.959 0.931 0.891

6

5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998 0.996 0.992 0.984

7

0 0.932 0.868 0.808 0.751 0.698 0.648 0.602 0.558 0.517 0.478 0.321 0.210 0.133 0.082 0.049 0.028 0.015 0.008

7

1 0.998 0.992 0.983 0.971 0.956 0.938 0.919 0.897 0.875 0.850 0.717 0.577 0.445 0.329 0.234 0.159 0.102 0.063

7

2 1.000 1.000 0.999 0.998 0.996 0.994 0.990 0.986 0.981 0.974 0.926 0.852 0.756 0.647 0.532 0.420 0.316 0.227

7

3 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.998 0.997 0.988 0.967 0.929 0.874 0.800 0.710 0.608 0.500

7

4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.995 0.987 0.971 0.944 0.904 0.847 0.773

7

5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.996 0.991 0.981 0.964 0.938

7

6 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998 0.996 0.992

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Continued

605



Binomial distribution (continued) p

n

x

8

0 0.923 0.851 0.784 0.721 0.663 0.610 0.560 0.513 0.470 0.430 0.272 0.168 0.100 0.058 0.032 0.017 0.008 0.004

8

1 0.997 0.990 0.978 0.962 0.943 0.921 0.897 0.870 0.842 0.813 0.657 0.503 0.367 0.255 0.169 0.106 0.063 0.035

8

2 1.000 1.000 0.999 0.997 0.994 0.990 0.985 0.979 0.971 0.962 0.895 0.797 0.679 0.552 0.428 0.315 0.220 0.145

8

3 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.998 0.997 0.995 0.979 0.944 0.886 0.806 0.706 0.594 0.477 0.363

8

4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.990 0.973 0.942 0.894 0.826 0.740 0.637

8

5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.996 0.989 0.975 0.950 0.912 0.855

8

6 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.996 0.991 0.982 0.965

8

7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998 0.996

9

0 0.914 0.834 0.760 0.693 0.630 0.573 0.520 0.472 0.428 0.387 0.232 0.134 0.075 0.040 0.021 0.010 0.005 0.002

9

1 0.997 0.987 0.972 0.952 0.929 0.902 0.873 0.842 0.809 0.775 0.599 0.436 0.300 0.196 0.121 0.071 0.039 0.020

9

2 1.000 0.999 0.998 0.996 0.992 0.986 0.979 0.970 0.960 0.947 0.859 0.738 0.601 0.463 0.337 0.232 0.150 0.090

9

3 1.000 1.000 1.000 1.000 0.999 0.999 0.998 0.996 0.994 0.992 0.966 0.914 0.834 0.730 0.609 0.483 0.361 0.254

9

4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.994 0.980 0.951 0.901 0.828 0.733 0.621 0.500

9

5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.997 0.990 0.975 0.946 0.901 0.834 0.746

9

6 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.996 0.989 0.975 0.950 0.910

9

7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.996 0.991 0.980

9

8 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.998

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

10 0 0.904 0.817 0.737 0.665 0.599 0.539 0.484 0.434 0.389 0.349 0.197 0.107 0.056 0.028 0.013 0.006 0.003 0.001 10 1 0.996 0.984 0.965 0.942 0.914 0.882 0.848 0.812 0.775 0.736 0.544 0.376 0.244 0.149 0.086 0.046 0.023 0.011 10 2 1.000 0.999 0.997 0.994 0.988 0.981 0.972 0.960 0.946 0.930 0.820 0.678 0.526 0.383 0.262 0.167 0.100 0.055 10 3 1.000 1.000 1.000 1.000 0.999 0.998 0.996 0.994 0.991 0.987 0.950 0.879 0.776 0.650 0.514 0.382 0.266 0.172 10 4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.999 0.998 0.990 0.967 0.922 0.850 0.751 0.633 0.504 0.377 10 5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.994 0.980 0.953 0.905 0.834 0.738 0.623



Appendix K Chi-Square Distribution

Chi-square distribution df

χ20.995

χ20.99

χ20.975

χ20.95

χ20.90

χ20.10

χ20.05

χ20.025

χ20.01

χ20.005

1

0.000

0.000

0.001

0.004

0.016

2.706

3.841

5.024

6.635

7.879

2

0.010

0.020

0.051

0.103

0.211

4.605

5.991

7.378

9.210

10.597

3

0.072

0.115

0.216

0.352

0.584

6.251

7.815

9.348

11.345

12.838

4

0.207

0.297

0.484

0.711

1.064

7.779

9.488

11.143

13.277

14.860

5

0.412

0.554

0.831

1.145

1.610

9.236

11.070

12.832

15.086

16.750

6

0.676

0.872

1.237

1.635

2.204

10.645

12.592

14.449

16.812

18.548

7

0.989

1.239

1.690

2.167

2.833

12.017

14.067

16.013

18.475

20.278

8

1.344

1.647

2.180

2.733

3.490

13.362

15.507

17.535

20.090

21.955

9

1.735

2.088

2.700

3.325

4.168

14.684

16.919

19.023

21.666

23.589

10

2.156

2.558

3.247

3.940

4.865

15.987

18.307

20.483

23.209

25.188

11

2.603

3.053

3.816

4.575

5.578

17.275

19.675

21.920

24.725

26.757

12

3.074

3.571

4.404

5.226

6.304

18.549

21.026

23.337

26.217

28.300

13

3.565

4.107

5.009

5.892

7.041

19.812

22.362

24.736

27.688

29.819

14

4.075

4.660

5.629

6.571

7.790

21.064

23.685

26.119

29.141

31.319

15

4.601

5.229

6.262

7.261

8.547

22.307

24.996

27.488

30.578

32.801

16

5.142

5.812

6.908

7.962

9.312

23.542

26.296

28.845

32.000

34.267

17

5.697

6.408

7.564

8.672

10.085

24.769

27.587

30.191

33.409

35.718

18

6.265

7.015

8.231

9.390

10.865

25.989

28.869

31.526

34.805

37.156

19

6.844

7.633

8.907

10.117

11.651

27.204

30.144

32.852

36.191

38.582

20

7.434

8.260

9.591

10.851

12.443

28.412

31.410

34.170

37.566

39.997

21

8.034

8.897

10.283

11.591

13.240

29.615

32.671

35.479

38.932

41.401

22

8.643

9.542

10.982

12.338

14.041

30.813

33.924

36.781

40.289

42.796

23

9.260

10.196

11.689

13.091

14.848

32.007

35.172

38.076

41.638

44.181

24

9.886

10.856

12.401

13.848

15.659

33.196

36.415

39.364

42.980

45.558

25

10.520

11.524

13.120

14.611

16.473

34.382

37.652

40.646

44.314

46.928

26

11.160

12.198

13.844

15.379

17.292

35.563

38.885

41.923

45.642

48.290

27

11.808

12.878

14.573

16.151

18.114

36.741

40.113

43.195

46.963

49.645

28

12.461

13.565

15.308

16.928

18.939

37.916

41.337

44.461

48.278

50.994 Continued

607



Chi-square distribution (continued) df

χ20.995

χ20.99

χ20.975

χ20.95

χ20.90

χ20.10

χ20.05

χ20.025

χ20.01

χ20.005

29

13.121

14.256

16.047

17.708

19.768

39.087

42.557

45.722

49.588

52.335

30

13.787

14.953

16.791

18.493

20.599

40.256

43.773

46.979

50.892

53.672

31

14.458

15.655

17.539

19.281

21.434

41.422

44.985

48.232

52.191

55.002

32

15.134

16.362

18.291

20.072

22.271

42.585

46.194

49.480

53.486

56.328

33

15.815

17.073

19.047

20.867

23.110

43.745

47.400

50.725

54.775

57.648

34

16.501

17.789

19.806

21.664

23.952

44.903

48.602

51.966

56.061

58.964

35

17.192

18.509

20.569

22.465

24.797

46.059

49.802

53.203

57.342

60.275

40

20.707

22.164

24.433

26.509

29.051

51.805

55.758

59.342

63.691

66.766

45

24.311

25.901

28.366

30.612

33.350

57.505

61.656

65.410

69.957

73.166

50

27.991

29.707

32.357

34.764

37.689

63.167

67.505

71.420

76.154

79.490

55

31.735

33.571

36.398

38.958

42.060

68.796

73.311

77.380

82.292

85.749

60

35.534

37.485

40.482

43.188

46.459

74.397

79.082

83.298

88.379

91.952

65

39.383

41.444

44.603

47.450

50.883

79.973

84.821

89.177

94.422

98.105

70

43.275

45.442

48.758

51.739

55.329

85.527

90.531

95.023 100.425 104.215

75

47.206

49.475

52.942

56.054

59.795

91.061

96.217 100.839 106.393 110.285

80

51.172

53.540

57.153

60.391

64.278

96.578 101.879 106.629 112.329 116.321

85

55.170

57.634

61.389

64.749

68.777

102.079 107.522 112.393 118.236 122.324

90

59.196

61.754

65.647

69.126

73.291

107.565 113.145 118.136 124.116 128.299

95

63.250

65.898

69.925

73.520

77.818

113.038 118.752 123.858 129.973 134.247

100

67.328

70.065

74.222

77.929

82.358

118.498 124.342 129.561 135.807 140.170



Appendix L Exponential Distribution

Exponential distribution X

Area to left of X

Area to right of X

0

0.00000

1.00000

0.1

0.09516

0.90484

0.2

0.18127

0.81873

0.3

0.25918

0.74082

0.4

0.32968

0.67032

0.5

0.39347

0.60653

0.6

0.45119

0.54881

0.7

0.50341

0.49659

0.8

0.55067

0.44933

0.9

0.59343

0.40657

1

0.63212

0.36788

1.1

0.66713

0.33287

1.2

0.69881

0.30119

1.3

0.72747

0.27253

1.4

0.75340

0.24660

1.5

0.77687

0.22313

1.6

0.79810

0.20190

1.7

0.81732

0.18268

1.8

0.83470

0.16530 0.14957

1.9

0.85043

2

0.86466

0.13534

2.1

0.87754

0.12246

2.2

0.88920

0.11080

2.3

0.89974

0.10026

2.4

0.90928

0.09072

2.5

0.91792

0.08208

2.6

0.92573

0.07427

Continued

609



Exponential distribution (continued) X

Area to left of X

Area to right of X

2.7

0.93279

0.06721

2.8

0.93919

0.06081

2.9

0.94498

0.05502

3

0.95021

0.04979

3.1

0.95495

0.04505

3.2

0.95924

0.04076

3.3

0.96312

0.03688

3.4

0.96663

0.03337

3.5

0.96980

0.03020

3.6

0.97268

0.02732

3.7

0.97528

0.02472

3.8

0.97763

0.02237

3.9

0.97976

0.02024

4

0.98168

0.01832

4.1

0.98343

0.01657

4.2

0.98500

0.01500

4.3

0.98643

0.01357

4.4

0.98772

0.01228

4.5

0.98889

0.01111

4.6

0.98995

0.01005

4.7

0.99090

0.00910

4.8

0.99177

0.00823

4.9

0.99255

0.00745

5

0.99326

0.00674

5.1

0.99390

0.00610

5.2

0.99448

0.00552

5.3

0.99501

0.00499

5.4

0.99548

0.00452

5.5

0.99591

0.00409

5.6

0.99630

0.00370

5.7

0.99665

0.00335

5.8

0.99697

0.00303

5.9

0.99726

0.00274

6

0.99752

0.00248



Appendix M Poisson Distribution Probability of x or fewer occurrences of an event

Poisson distribution x↓ n→

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

0.005 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.01

0.990 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.02

0.980 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.03

0.970 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.04

0.961 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.05

0.951 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.06

0.942 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.07

0.932 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.08

0.923 0.997 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.09

0.914 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.1

0.905 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.15

0.861 0.990 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.2

0.819 0.982 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.25

0.779 0.974 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.3

0.741 0.963 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.35

0.705 0.951 0.994 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.4

0.670 0.938 0.992 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.5

0.607 0.910 0.986 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.6

0.549 0.878 0.977 0.997 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.7

0.497 0.844 0.966 0.994 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.8

0.449 0.809 0.953 0.991 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0.9

0.407 0.772 0.937 0.987 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1

0.368 0.736 0.920 0.981 0.996 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.2

0.301 0.663 0.879 0.966 0.992 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.4

0.247 0.592 0.833 0.946 0.986 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.6

0.202 0.525 0.783 0.921 0.976 0.994 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.8

0.165 0.463 0.731 0.891 0.964 0.990 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2

0.135 0.406 0.677 0.857 0.947 0.983 0.995 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Continued

611



Poisson distribution (continued) x↓ n→

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

2.2

0.111 0.355 0.623 0.819 0.928 0.975 0.993 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2.4

0.091 0.308 0.570 0.779 0.904 0.964 0.988 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2.6

0.074 0.267 0.518 0.736 0.877 0.951 0.983 0.995 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2.8

0.061 0.231 0.469 0.692 0.848 0.935 0.976 0.992 0.998 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3

0.050 0.199 0.423 0.647 0.815 0.916 0.966 0.988 0.996 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3.2

0.041 0.171 0.380 0.603 0.781 0.895 0.955 0.983 0.994 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3.4

0.033 0.147 0.340 0.558 0.744 0.871 0.942 0.977 0.992 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3.6

0.027 0.126 0.303 0.515 0.706 0.844 0.927 0.969 0.988 0.996 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3.8

0.022 0.107 0.269 0.473 0.668 0.816 0.909 0.960 0.984 0.994 0.998 0.999 1.000 1.000 1.000 1.000 1.000 1.000

4

0.018 0.092 0.238 0.433 0.629 0.785 0.889 0.949 0.979 0.992 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000

4.5

0.011 0.061 0.174 0.342 0.532 0.703 0.831 0.913 0.960 0.983 0.993 0.998 0.999 1.000 1.000 1.000 1.000 1.000

5

0.007 0.040 0.125 0.265 0.440 0.616 0.762 0.867 0.932 0.968 0.986 0.995 0.998 0.999 1.000 1.000 1.000 1.000

5.5

0.004 0.027 0.088 0.202 0.358 0.529 0.686 0.809 0.894 0.946 0.975 0.989 0.996 0.998 0.999 1.000 1.000 1.000

6

0.002 0.017 0.062 0.151 0.285 0.446 0.606 0.744 0.847 0.916 0.957 0.980 0.991 0.996 0.999 0.999 1.000 1.000

6.5

0.002 0.011 0.043 0.112 0.224 0.369 0.527 0.673 0.792 0.877 0.933 0.966 0.984 0.993 0.997 0.999 1.000 1.000

7

0.001 0.007 0.030 0.082 0.173 0.301 0.450 0.599 0.729 0.830 0.901 0.947 0.973 0.987 0.994 0.998 0.999 1.000

7.5

0.001 0.005 0.020 0.059 0.132 0.241 0.378 0.525 0.662 0.776 0.862 0.921 0.957 0.978 0.990 0.995 0.998 0.999

8

0.000 0.003 0.014 0.042 0.100 0.191 0.313 0.453 0.593 0.717 0.816 0.888 0.936 0.966 0.983 0.992 0.996 0.998

8.5

0.000 0.002 0.009 0.030 0.074 0.150 0.256 0.386 0.523 0.653 0.763 0.849 0.909 0.949 0.973 0.986 0.993 0.997

9

0.000 0.001 0.006 0.021 0.055 0.116 0.207 0.324 0.456 0.587 0.706 0.803 0.876 0.926 0.959 0.978 0.989 0.995

9.5

0.000 0.001 0.004 0.015 0.040 0.089 0.165 0.269 0.392 0.522 0.645 0.752 0.836 0.898 0.940 0.967 0.982 0.991

10

0.000 0.000 0.003 0.010 0.029 0.067 0.130 0.220 0.333 0.458 0.583 0.697 0.792 0.864 0.917 0.951 0.973 0.986

10.5

0.000 0.000 0.002 0.007 0.021 0.050 0.102 0.179 0.279 0.397 0.521 0.639 0.742 0.825 0.888 0.932 0.960 0.978



Appendix N Median Ranks

Median ranks n

1

2

3

4

5

6

7

8

9

10

11

12

1

0.500

0.292

0.206

0.159

0.130

0.109

0.095

0.083

0.074

0.067

0.061

0.056

0.708

0.500

0.386

0.315

0.266

0.230

0.202

0.181

0.163

0.149

0.137

0.794

0.614

0.500

0.422

0.365

0.321

0.287

0.260

0.237

0.218

0.841

0.685

0.578

0.500

0.440

0.394

0.356

0.325

0.298

0.870

0.734

0.635

0.560

0.500

0.452

0.412

0.379

0.891

0.770

0.679

0.606

0.548

0.500

0.460

0.905

0.798

0.713

0.644

0.588

0.540

0.917

0.819

0.740

0.675

0.621

0.926

0.837

0.763

0.702

0.933

0.851

0.782

0.939

0.863

2 3 4 5 6 7 8 9 10 11 12

0.944

n

13

14

15

16

17

18

19

20

21

22

23

24

1

0.052

0.049

0.045

0.043

0.040

0.038

0.036

0.034

0.033

0.031

0.030

0.029

2

0.127

0.118

0.110

0.104

0.098

0.092

0.088

0.083

0.079

0.076

0.073

0.070

3

0.201

0.188

0.175

0.165

0.155

0.147

0.139

0.132

0.126

0.121

0.115

0.111

4

0.276

0.257

0.240

0.226

0.213

0.201

0.191

0.181

0.173

0.165

0.158

0.152

5

0.351

0.326

0.305

0.287

0.270

0.255

0.242

0.230

0.220

0.210

0.201

0.193

6

0.425

0.396

0.370

0.348

0.328

0.310

0.294

0.279

0.266

0.254

0.244

0.234

7

0.500

0.465

0.435

0.409

0.385

0.364

0.345

0.328

0.313

0.299

0.286

0.275

8

0.575

0.535

0.500

0.470

0.443

0.418

0.397

0.377

0.360

0.344

0.329

0.316

9

0.649

0.604

0.565

0.530

0.500

0.473

0.448

0.426

0.407

0.388

0.372

0.357

10

0.724

0.674

0.630

0.591

0.557

0.527

0.500

0.475

0.453

0.433

0.415

0.398

11

0.799

0.743

0.695

0.652

0.615

0.582

0.552

0.525

0.500

0.478

0.457

0.439

12

0.873

0.813

0.760

0.713

0.672

0.636

0.603

0.574

0.547

0.522

0.500

0.480

Continued

613



Median ranks (continued) n

13

14

15

16

17

18

19

20

21

22

23

24

13

0.948

0.882

0.825

0.774

0.730

0.690

0.655

0.623

0.593

0.567

0.543

0.520

0.951

0.890

0.835

0.787

0.745

0.706

0.672

0.640

0.612

0.585

0.561

0.955

0.896

0.845

0.799

0.758

0.721

0.687

0.656

0.628

0.602

0.957

0.902

0.853

0.809

0.770

0.734

0.701

0.671

0.643

0.960

0.908

0.861

0.819

0.780

0.746

0.714

0.684

0.962

0.912

0.868

0.827

0.790

0.756

0.725

0.964

0.917

0.874

0.835

0.799

0.766

0.966

0.921

0.879

0.842

0.807

0.967

0.924

0.885

0.848

0.969

0.927

0.889

0.970

0.930

14 15 16 17 18 19 20 21 22 23 24

0.971



Appendix O Normal Scores

Normal scores n=

4

5

6

7

8

9

10

11

12

13

14

15

16

17

1

-1.05

-1.18

-1.28

-1.36

-1.43

-1.50

-1.55

-1.59

-1.64

-1.68

-1.71

-1.74

-1.77

-1.80

2

-0.30

-0.50

-0.64

-0.76

-0.85

-0.93

-1.00

-1.06

-1.11

-1.16

-1.20

-1.24

-1.28

-1.32

3

0.30

0.00

-0.20

-0.35

-0.47

-0.57

-0.65

-0.73

-0.79

-0.85

-0.90

-0.94

-0.99

-1.03

4

1.05

0.50

0.20

0.00

-0.15

-0.27

-0.37

-0.46

-0.53

-0.60

-0.66

-0.71

-0.76

-0.80

1.18

0.64

0.35

0.15

0.00

-0.12

-0.22

-0.31

-0.39

-0.45

-0.51

-0.57

-0.62

1.28

0.76

0.47

0.27

0.12

0.00

-0.10

-0.19

-0.27

-0.33

-0.39

-0.45

1.36

0.85

0.57

0.37

0.22

0.10

0.00

-0.09

-0.16

-0.23

-0.29

1.43

0.93

0.65

0.46

0.31

0.19

0.09

0.00

-0.08

-0.15

1.50

1.00

0.73

0.53

0.39

0.27

0.16

0.08

0.00

1.55

1.06

0.79

0.60

0.45

0.33

0.23

0.15

1.59

1.11

0.85

0.66

0.51

0.39

0.29

1.64

1.16

0.90

0.71

0.57

0.45

1.68

1.20

0.94

0.76

0.62

1.71

1.24

0.99

0.80

1.74

1.28

1.03

1.77

1.32

5 6 7 8 9 10 11 12 13 14 15 16 17

1.80

18 19 20 21 22 23 24 25 26 27 28 29 30

Continued

615



Normal scores (continued) n=

18

19

20

21

22

23

24

25

26

27

28

29

30

1

-1.82

-1.85

-1.87

-1.89

-1.91

-1.93

-1.95

-1.97

-1.98

-2.00

-2.01

-2.03

-2.04

2

-1.35

-1.38

-1.40

-1.43

-1.45

-1.48

-1.50

-1.52

-1.54

-1.56

-1.58

-1.59

-1.61

3

-1.06

-1.10

-1.13

-1.16

-1.18

-1.21

-1.24

-1.26

-1.28

-1.30

-1.32

-1.34

-1.36

4

-0.84

-0.88

-0.92

-0.95

-0.98

-1.01

-1.04

-1.06

-1.09

-1.11

-1.13

-1.15

-1.17

5

-0.66

-0.70

-0.74

-0.78

-0.81

-0.84

-0.87

-0.90

-0.93

-0.95

-0.98

-1.00

-1.02

6

-0.50

-0.54

-0.59

-0.63

-0.66

-0.70

-0.73

-0.76

-0.79

-0.82

-0.84

-0.87

-0.89

7

-0.35

-0.40

-0.45

-0.49

-0.53

-0.57

-0.60

-0.63

-0.66

-0.69

-0.72

-0.75

-0.77

8

-0.21

-0.26

-0.31

-0.36

-0.40

-0.44

-0.48

-0.52

-0.55

-0.58

-0.61

-0.64

-0.67

9

-0.07

-0.13

-0.19

-0.24

-0.28

-0.33

-0.37

-0.41

-0.44

-0.48

-0.51

-0.54

-0.57

10

0.07

0.00

-0.06

-0.12

-0.17

-0.22

-0.26

-0.30

-0.34

-0.38

-0.41

-0.44

-0.47

11

0.21

0.13

0.06

0.00

-0.06

-0.11

-0.15

-0.20

-0.24

-0.28

-0.31

-0.35

-0.38

12

0.35

0.26

0.19

0.12

0.06

0.00

-0.05

-0.10

-0.14

-0.18

-0.22

-0.26

-0.29

13

0.50

0.40

0.31

0.24

0.17

0.11

0.05

0.00

-0.05

-0.09

-0.13

-0.17

-0.21

14

0.66

0.54

0.45

0.36

0.28

0.22

0.15

0.10

0.05

0.00

-0.04

-0.09

-0.12

15

0.84

0.70

0.59

0.49

0.40

0.33

0.26

0.20

0.14

0.09

0.04

0.00

-0.04

16

1.06

0.88

0.74

0.63

0.53

0.44

0.37

0.30

0.24

0.18

0.13

0.09

0.04

17

1.35

1.10

0.92

0.78

0.66

0.57

0.48

0.41

0.34

0.28

0.22

0.17

0.12

18

1.82

1.38

1.13

0.95

0.81

0.70

0.60

0.52

0.44

0.38

0.31

0.26

0.21

1.85

1.40

1.16

0.98

0.84

0.73

0.63

0.55

0.48

0.41

0.35

0.29

1.87

1.43

1.18

1.01

0.87

0.76

0.66

0.58

0.51

0.44

0.38

1.89

1.45

1.21

1.04

0.90

0.79

0.69

0.61

0.54

0.47

1.91

1.48

1.24

1.06

0.93

0.82

0.72

0.64

0.57

1.93

1.50

1.26

1.09

0.95

0.84

0.75

0.67

1.95

1.52

1.28

1.11

0.98

0.87

0.77

1.97

1.54

1.30

1.13

1.00

0.89

1.98

1.56

1.32

1.15

1.02

2.00

1.58

1.34

1.17

2.01

1.59

1.36

2.03

1.61

19 20 21 22 23 24 25 26 27 28 29 30

2.04



Appendix P Values of t Distribution

Values of t distribution df

t 0.10

t 0.05

t 0.025

t 0.01

t 0.005

df

1

3.078

6.314

12.706

31.821

63.656

1

2

1.886

2.920

4.303

6.965

9.925

2

3

1.638

2.353

3.182

4.541

5.841

3

4

1.533

2.132

2.776

3.747

4.604

4

5

1.476

2.015

2.571

3.365

4.032

5

6

1.440

1.943

2.447

3.143

3.707

6

7

1.415

1.895

2.365

2.998

3.499

7

8

1.397

1.860

2.306

2.896

3.355

8

9

1.383

1.833

2.262

2.821

3.250

9

10

1.372

1.812

2.228

2.764

3.169

10

11

1.363

1.796

2.201

2.718

3.106

11

12

1.356

1.782

2.179

2.681

3.055

12

13

1.350

1.771

2.160

2.650

3.012

13

14

1.345

1.761

2.145

2.624

2.977

14

15

1.341

1.753

2.131

2.602

2.947

15

16

1.337

1.746

2.120

2.583

2.921

16

17

1.333

1.740

2.110

2.567

2.898

17

18

1.330

1.734

2.101

2.552

2.878

18

19

1.328

1.729

2.093

2.539

2.861

19

20

1.325

1.725

2.086

2.528

2.845

20

21

1.323

1.721

2.080

2.518

2.831

21

22

1.321

1.717

2.074

2.508

2.819

22

23

1.319

1.714

2.069

2.500

2.807

23

24

1.318

1.711

2.064

2.492

2.797

24

25

1.316

1.708

2.060

2.485

2.787

25

26

1.315

1.706

2.056

2.479

2.779

26

27

1.314

1.703

2.052

2.473

2.771

27

28

1.313

1.701

2.048

2.467

2.763

28

Continued

617



Values of t distribution (continued) df

t 0.10

t 0.05

t 0.025

t 0.01

t 0.005

df

29

1.311

1.699

2.045

2.462

2.756

29

30

1.310

1.697

2.042

2.457

2.750

30

31

1.309

1.696

2.040

2.453

2.744

31

32

1.309

1.694

2.037

2.449

2.738

32

33

1.308

1.692

2.035

2.445

2.733

33

34

1.307

1.691

2.032

2.441

2.728

34

35

1.306

1.690

2.030

2.438

2.724

35

40

1.303

1.684

2.021

2.423

2.704

40

45

1.301

1.679

2.014

2.412

2.690

45

50

1.299

1.676

2.009

2.403

2.678

50

55

1.297

1.673

2.004

2.396

2.668

55

60

1.296

1.671

2.000

2.390

2.660

60

70

1.294

1.667

1.994

2.381

2.648

70

80

1.292

1.664

1.990

2.374

2.639

80

90

1.291

1.662

1.987

2.368

2.632

90

100

1.290

1.660

1.984

2.364

2.626

100

200

1.286

1.653

1.972

2.345

2.601

200

400

1.284

1.649

1.966

2.336

2.588

400

600

1.283

1.647

1.964

2.333

2.584

600

800

1.283

1.647

1.963

2.331

2.582

800

999

1.282

1.646

1.962

2.330

2.581

999



Appendix Q Selected National and International Quality System Standards

American National Standards Institute* 1430 Broadway New York, NY 10018 ANSI/ASQ Z1.4-2003 Sampling Procedures and Tables for Inspection by Attributes ANSI/ASQ Z1.9-2003 Sampling Procedures and Tables for Inspection by Variables for Percent Nonconforming ANSI/ASQC C1-1996 (ANSI Z1.8-1971) Specifications of General Requirements for a Quality Program ANSI/ASQC D1160-1995 Formal Design Review ANSI/ISO/ASQ E14001-2004 Environmental management systems—Requirements with guidance for use ANSI/ISO/ASQ Q9000-2005 Quality management systems—Fundamentals and vocabulary ANSI/ISO/ASQ Q9001-2008 Quality management systems—Requirements ANSI/ISO/ASQ Q9004-2000 Quality management systems—Guidelines for performance improvements ANSI/ISO/ASQ Q10002-2004 Quality management—Customer satisfaction— Guidelines for complaints handling in organizations ANSI/ISO/ASQ Q10005-2005 Quality management—Guidelines for quality plans ANSI/ISO/ASQ Q10006-2003 Quality management—Guidelines for quality management in projects ANSI/ISO/ASQ Q10007-2003 Quality management systems—Guidelines for configuration management ANSI/ISO/ASQ QE19011S-2008 Guidelines for management systems auditing— U.S. version with supplemental guidance added * Copies of these standards can be ordered from American Society for Quality (ASQ), PO Box 3005, Milwaukee, WI 53201-3005, or may be downloaded from www.e-standards.asq.org.

619



ANSI/ISO/ASQC Q9000-1-1994 Quality management and quality assurance standards—Guidelines for selection and use ANSI/ISO/ASQC Q9001-1994 Quality systems—Model for quality assurance in design, development, production, installation, and servicing ANSI/ISO/ASQC Q9002-1994: Quality systems—Model for quality assurance in production, installation, and servicing ANSI/ISO/ASQC Q9003-1994: Quality systems—Model for quality assurance in final inspection and test

North Atlantic Treaty Organization Autoroute De Zaventem 1110 NATO (Brussels), Belgium AQAP-1: NATO Requirements for an Industrial Quality Control System AQAP-2: Guide for the Evaluation of a Contractor’s Quality Control System for Compliance with AQAP-1 AQAP-4: NATO Inspection Systems Requirements for Industry AQAP-5: Guide for the Evaluation of a Contractor’s Inspection System for Compliance with AQAP-4 AQAP-7: Guide for the Evaluation of a Contractor’s Measurement and Calibration System for Compliance with AQAP-6 IEC Guide 102 (1996-03): Specifications Structure for Quality

British Standards Institution 101 Pentonville Road London N19ND, England BSI HDBK 22-1981: Quality Assurance (Contains 15 Publications)

Canadian Standards Association (CSA) 178 Rexdale Boulevard Rexdale, Ontario Canada M9W IR3 CAN3 Z299-1—CSA: Quality Assurance Program—Category 1 CAN3 Z299-2—CSA: Quality Assurance Program—Category 2 CAN3 Z299-3—CSA: Quality Assurance Program—Category 3


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Appendix Q: Selected National and International Quality System Standards 621

International Organization for Standardization (ISO)* 1, rue de Varembé, Case postale 56 CH-1211 Geneva 20, Switzerland ISO 9000-1-1994: Quality management and quality assurance standards—Part 1: Guidelines for selection and use ISO 9000-2-1997: Quality management and quality assurance standards—Part 2: Generic guidelines for the application of ISO 9001, ISO 9002, and ISO 9003 ISO 9000-3-1997: Quality management and quality assurance standards—Part 3: Guidelines for the application of ISO 9001:1994 to the development, supply, installation, and maintenance of computer software ISO 9000-4-1993: Quality management and quality assurance standards—Part 4: Guide to dependability programme management ISO 10019-2005: Guidelines for the selection of quality management system consultants and use of their services ISO 13485-2003: Medical devices: Quality management systems—Requirements for regulatory purposes ISO/TR 10013-2001: Guidelines for quality management system documentation ISO/TR 13352-1997: Guidelines for interpretation of ISO 9000 series for application within the iron ore industry

The Department of Defense (DOD) The Pentagon Washington, DC 20301-1155 MIL-HDBK-50: Evaluation of a Contractor’s Quality Program MIL-Q-9858A: Quality Program Requirements MIL-STD-1521B: Technical Reviews and Audits of System, Equipment, and Computer Software MIL-STD-1535A: Supplier Quality Assurance Quality Requirements MIL-STD-2164: Failure Reporting, Analysis, and Corrective Action Systems MIL-T-50301: Quality Control System Requirements for Technical Data

* Copies of these standards can be ordered from American Society for Quality (ASQ), PO Box 3005, Milwaukee, WI 53201-3005, or may be downloaded from www.e-standards.asq.org.





Glossary

A acceptable quality level (AQL)—The maximum percentage or proportion of variant units in a lot or batch that, for purposes of acceptance sampling, can be considered satisfactory as a process average. acceptance sampling—Sampling inspection in which decisions are made to accept or not accept a product or service; also, the methodology that deals with procedures by which decisions to accept or not accept are based on the results of the inspection of samples. accuracy—A qualitative term that describes the closeness of alignment between an observed value and an accepted reference value. action plan—The detailed plan to implement the actions needed to achieve strategic goals and objectives. activity—An action of some type that requires a time duration for accomplishment. activity network diagram (AND) (arrow diagram)—A management and planning tool used to develop the best possible schedule and appropriate controls to accomplish the schedule; the critical path method (CPM) and the program evaluation review technique (PERT) make use of arrow diagrams. advanced product quality planning (APQP) and control plan—APQP is a comprehensive quality planning and control system specifying protocols for product and process design and development, validation, assessment, and corrective action. advanced quality planning (AQP)—A comprehensive system of applying quality disciplines during a product or process development effort; sometimes also called advanced product quality planning (APQP). alternative hypothesis—In statistical hypothesis testing, this is the hypothesis that the null hypothesis is tested against. The hypothesis test is conducted under the assumption that the null hypothesis is true. If evidence is found against the null hypothesis, the alternative hypothesis is accepted.

623


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 624 Glossary

analysis of variance (ANOVA)—A partitioning of total variability into components due to factors or other sources of variation. The sources of variation as well as their corresponding sums of squares and degrees of freedom are usually given in an analysis of variance table. analytical study—A study that uses theory and a model in order to predict future outcomes or to lead to a change in outcomes. assignable cause—A factor that contributes to variation and that is feasible to detect and identify. assumptions—Conditions that must be true in order for a statistical procedure to be valid. attributes data—Data that are categorized for analysis or evaluation. (Attributes data may involve measurements as long as the measurements are used only to place a given piece of data in a category for further analysis or evaluation. Contrast with variables data.) auditee—The individual or organization being audited. availability—A measure of the degree to which an item is in the operable and committable state at the start of the mission when the mission is called for at an unknown (random) time. average outgoing quality (AOQ)—The expected quality of outgoing product following the use of an acceptance sampling plan for a given value of incoming product quality. average outgoing quality limit (AOQL)—For a given acceptance sampling plan, the maximum AOQ over all possible levels of incoming quality. average run length (ARL)—In process monitoring and statistical process control, the average number of time periods or samples that elapse until the process signals out-of-control or produces an out-of-control signal. average sample number (ASN)—The average number of sample units per lot used for making decisions (acceptance or nonacceptance).

B benchmark—An organization, part of an organization, or measurement that serves as a reference point or point of comparison. benefit–cost analysis—A collection of the dollar value of benefits derived from an initiative, divided by the associated costs incurred. bias—A quantitative term representing the systematic difference between results or measurements obtained and the true quantity of interest. In measurement system analysis, bias describes the difference between the average of measurements made on the same unit and its reference or master value.


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Glossary 625

Bernoulli trials—A set of n independent trials that will result in one of two possible outcomes, usually defined as “success” and “failure.” The probability of a success is constant from trial to trial. binomial distribution—A discrete distribution describing the number of successes in a set or series of n Bernoulli trials. bivariate distribution—A joint distribution of two random variables. The joint distribution of two normally distributed random variables is the bivariate normal distribution. block diagram—A diagram that describes the operation, interrelationships, and interdependencies of components in a system. Boxes, or blocks (hence the name), represent the components; connecting lines between the blocks represent interfaces. There are two types of block diagrams: a functional block diagram, which shows a system’s subsystems and lower-level products, their interrelationships, and interfaces with other systems, and a reliability block diagram, which is similar to the functional block diagram except that it is modified to emphasize those aspects influencing reliability. box plot—A graphical method for displaying characteristics of a set of data. The box represents the interquartile range (middle 50 percent of the data). The whiskers extend from each end of the box to some specified bounds. Also known as the box-and-whisker plot. brainstorming—A problem-solving tool that teams use to generate as many ideas as possible related to a particular subject. Team members begin by offering all their ideas; the ideas are not discussed or reviewed until after the brainstorming session.

C calibration—The comparison of a measurement instrument or system of unverified accuracy to a measurement instrument or system of known accuracy to detect any variation from the true value. categorical variable—A variable whose possible outcomes are categories that have no numerical significance. The data are counts that can be classified into different categories. causal factor—A variable that when changed or manipulated in some manner serves to influence a given effect or result. cause-and-effect diagram—A graphical aid used to organize and identify possible causes of a problem or effect. Also known as a fishbone diagram or Ishikawa diagram. chance cause variation—Variation due to chance causes. Also known as common cause or random variation.



change agent—The person who takes the lead in transforming a company into a quality organization by providing guidance during the planning phase, facilitating implementation, and supporting those who pioneer the changes. characteristic—A property that helps to differentiate between items of a given sample or population. client—A person or organization requesting an audit. confidence coefficient—Associated with confidence intervals, the confidence coefficient represents the probability that the given interval of values will contain the true parameter of interest. Also known as the confidence level or level of confidence. confidence interval—An interval of values (L, U) that is believed to contain the true parameter value of interest with a certain probability, where L and U depend only on the sample observations. The probability level refers only to the interval constructed and its properties and not the unknown parameter being estimated. The interpretation of a 99 percent confidence interval would be that if the estimation procedure is repeated over and over again, then 99 percent of all the constructed intervals would contain the true parameter of interest. conflict resolution—A process for resolving disagreements in a manner acceptable to all parties. consensus—Finding a proposal acceptable enough that all team members can support the decision and no member opposes it. consumer’s risk (a )—For a sampling plan, refers to the probability of acceptance of a lot the quality of which has a designated numerical value representing a level that is seldom desirable. Usually the value will be the lot tolerance percent defective (LTPD). Also known as the beta risk and probability of a type II error. contingency table—A table of observations resulting from the classification of observations into two or more categories simultaneously. continuous variable—A variable whose possible values form an interval set of numbers such that between each two values in the set another member of the set occurs. control chart—A chart used to monitor a quality characteristic of interest. A control chart generally consists of three horizontal lines, one representing the mean or target level and two representing an upper limit and lower limit (although there are many instances when only an upper limit or only a lower limit are of interest). The limits are statistically determined. A process being monitored is considered out of control when points plot beyond the control limits. control plan—A document that may include the characteristics for quality of a product or service, measurements, and methods of control.



coordinate measuring machine (CMM)—Coordinate measuring machines (CMM) can most easily be defined as physical representations of a threedimensional rectilinear coordinate system. Coordinate measuring machines now represent a significant fraction of the measuring equipment used for defining the geometry of different-shaped workpieces. corrective action—Action taken to eliminate the root cause(s) and symptom(s) of an existing deviation or nonconformity to prevent recurrence. correlation—A general term describing the degree of interdependence between two or more variables. correlation coefficient—A measure of the linear relationship between two random variables. The correlation coefficient is dimensionless and can take on any value between –1 and 1, inclusive. Crawford slip method—A method of gathering and presenting anonymous data from a group by using various voting schemes. critical defect—A defect that judgment and experience indicate is likely to result in hazardous or unsafe conditions for the individuals using, maintaining, or depending on the product; or a defect that judgment and experience indicate is likely to prevent performance of the unit. critical path—The sequence of tasks that takes the longest time and determines a project’s completion date. critical path method (CPM)—An activity-oriented project management technique that uses arrow-diagramming techniques to demonstrate both the time and cost required to complete a project. It provides one time estimate—normal time. critical region—In hypothesis testing, it is the values of the test statistic that will lead to rejection of the null hypothesis. critical value—In hypothesis testing, it is the value (or values) to which the value of the test statistic is compared to determine if the null hypothesis can be rejected. The critical value is directly related to the significance level of the test. criticality—An indication of the consequences that are expected to result from a failure. cross-functional team—A group consisting of members from more than one department that is organized to accomplish a project. cumulative distribution function (cdf)—Used to describe a probability distribution for a random variable. For a random variable X, the cumulative distribution function would be given by P(X ≤ x) where x is some real value. cycle time—Refers to the time that it takes to complete a process from beginning to end.



D defect—A departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to satisfy intended normal or reasonably foreseeable usage requirements. density function—See probability density function. dependent events—Two events A and B are dependent if the probability of one event occurring is affected by the occurrence of the other event. deployment—To spread around. Used in strategic planning to describe the process of cascading plans throughout the organization. descriptive statistics—Techniques for displaying and summarizing data. Examples include histograms, run charts, mean, and standard deviation. design of experiments (DOE), designed experiment—The arrangement in which an experimental program is to be conducted and the selection of the levels of the factors or factor combinations to be included in the experiment. design review—Documented, comprehensive, and systematic examination of a design to evaluate its capability to fulfill the requirements for quality. detection—The likelihood of detecting a failure once it has occurred. Detection is evaluated based on a 10-point scale. At the lowest end of the scale (1) it is assumed that a design control will detect a failure with certainty. At the highest end of the scale (10) it is assumed that a design control will not detect a failure if a failure occurs. discrete variable—A variable whose possible values form a finite or at most countably infinite set. distribution function—See cumulative distribution function. DMAIC—An acronym denoting a sequence used in the methodology most often associated with Six Sigma: define, measure, analyze, improve, control.

E empowerment—A condition whereby employees have the authority to make decisions and take action in their work areas, within stated bounds, without prior approval. entity—Item that can be individually described and considered. error—1. Error in measurement is the difference between the estimated value and the true value of a measured quantity. 2. A fault resulting from defective judgment, deficient knowledge, or carelessness. It is not to be confused with measurement error, which is the difference between a computed or measured value and the true or theoretical value. error-proofing—See foolproofing.



estimate—A numerical value for a population parameter based on information collected from a sample. Also known as a point estimate. estimator—A statistic used as an estimate for a parameter. Also known as a point estimator. expected value—The mean of a random variable. external failure costs—Costs associated with defects found during or after delivery of the product or service.

F F distribution—A probability distribution of the ratio of two independent chi-square random variables divided by their respective degrees of freedom. facilitator—An individual who is responsible for creating favorable conditions that will enable a team to reach its purpose or achieve its goals by bringing together the necessary tools, information, and resources to get the job done. factor—An assignable cause that may affect the responses (test results) and of which different versions (levels) are included in the experiment. factorial design—In experimental design, a type of design where all possible combinations of factor levels are examined. failure—The termination, due to one or more defects, of the ability of an item, product, or service to perform its required function when called on to do so. A failure may be partial, complete, or intermittent. failure mode and effects analysis (FMEA)—A procedure in which each potential failure mode in every sub-item of an item is analyzed to determine its effect on other sub-items and on the required function of the item. filters—Relative to human-to-human communication, those perceptions (based on culture, language, demographics, experience, and so on) that affect how a message is transmitted by the sender and how a message is interpreted by the receiver. fishbone diagram—See cause-and-effect diagram. fixed effect—In analysis of variance, an effect is fixed if the factor levels included in the test or experiment are the only levels of interest. That is, the levels of the factor included in the experiment are the only ones to which the results of testing will apply. flowchart—A graphical representation of the steps in a process. Flowcharts are drawn to better understand processes. The flowchart is one of the seven tools of quality. foolproofing—A process of making a product or process immune to foolish errors on the part of the user or operator. Synonymous with error-proofing.



fraction nonconforming—In quality control, the proportion of the total number of units under study that are nonconforming. Also known as fraction defective. fractional factorial design—In experimental design, a design consisting of only a subset or fraction of all possible combinations of a factorial design.

G gage repeatability and reproducibility (GR&R)—Measures the capability of a gage to determine if it is suitable for use in its intended application. Repeatability represents the gage variability when it is used to measure the same unit with the same setup or operator. Reproducibility refers to the variability arising from different setups or operators. Gantt chart—A type of bar chart used in process/project planning and control to display planned work and finished work in relation to time. Also called a milestone chart. gatekeeping—The role of an individual (often a facilitator) in a group meeting in helping ensure effective interpersonal interactions (for example, to make sure someone’s ideas are not ignored due to the team moving on to the next topic too quickly). gauging—Gauging is a procedure that determines product conformance with specifications with the aid of measuring instruments such as calipers, micrometers, templates, and other mechanical, optical, and electronic devices. Gaussian distribution—Another name for the normal distribution, often attributed to Karl Gauss. goal—A statement of general intent, aim, or desire; it is the point toward which the organization (or individual) directs its efforts; goals are often nonquantitative.

H hierarchical relationship—A set of relationships that can be ordered or arranged from general to specific. histogram—A graphical display of observations from a sample where the class frequencies are represented by areas of rectangles over the interval for each class. hold point—A point, defined in an appropriate document, beyond which an activity must not proceed without the approval of a designated organization or authority. hypothesis testing—A method for testing a statement about a population using sample data. The statement to be tested may also concern a distributional form of a quality characteristic of interest.



I independent events—Two events A and B are said to be independent if the outcome of one event has no affect on the outcome of the other event. If two events are independent, then the probability that they both occur is the product of the probabilities of their individual occurrence. That is, P(A ∩ B) = P(A)P(B). inferential statistics—Techniques for reaching conclusions about a population based on analysis of data from a sample. information system—Technology-based system used to support operations, aid day-to-day decision making, and support strategic analysis (other names often used include management information system, decision system, information technology (IT), data processing). inspection—The process of measuring, examining, testing, gauging, or otherwise comparing a unit with the applicable requirements. interaction—A term used to represent the phenomenon where two or more variables do not function independently of one another. internal failure costs—Costs associated with defects found before the product or service is delivered. intervention—An action taken by a leader or a facilitator to support the effective functioning of a team or work group. Ishikawa diagram—See cause-and-effect diagram.

J joint distribution—A probability distribution representing two or more variables that are involved in a random experiment. Also known as joint probability distribution.

K kaizen blitz/event—An intense, short-time-frame, team approach to employing the concepts and techniques of continuous improvement (for example, to reduce cycle time or increase throughput).

L leader—An individual recognized by others as the person to lead an effort. One can not be a “leader” without one or more “followers.” The term is often used interchangeably with “manager.” A “leader” may or may not hold an officially designated management-type position. leadership—An essential part of a quality improvement effort. Organization leaders must establish a vision, communicate that vision to those in the organization, and provide the tools, knowledge, and motivation necessary to accomplish the vision.



least squares estimation—In regression analysis, a method for estimating parameters by minimizing the sum of the squared differences between the actual or observed responses and the values predicted by the fitted model. level of significance (` )—A stated or fixed probability of wrongly rejecting a true null hypothesis that the practitioner is willing to accept. It is the probability of committing a type I error. levels—In experimental design, the chosen values of a factor of interest to be varied in an experiment. lot tolerance percent defective (LTPD)—LTPD, expressed in percent defective, is the poorest quality in an individual lot that should be accepted.

M main effect—The effect on a response due to a change in a factor or variable independent of all other factors or variables in the system. maintainability—The measure of the ability of an item to be retained or restored to a specified condition when maintenance is performed by personnel having specified skill levels using prescribed procedures and resources at each prescribed level of maintenance and repair. major defect—A defect that will interfere with normal or reasonable foreseeable use, but will not cause a risk of damage or injury. material control—A broad collection of tools for managing the items and lots in a production process. materials review board—A quality control committee or team, usually employed in manufacturing or other materials-processing installations, that has the responsibility and authority to deal with items or materials that do not conform to fitness-for-use specifications. mean—A measure of central tendency. For random variables, it is also the expected value. For a sample of data of size n, it is the sum of the observations divided by n. mean squares—In analysis of variance, mean squares are estimates of variances. In general, they are found by dividing the sum of squares by the appropriate degrees of freedom. mean time between failures (MTBF)—A basic measure of reliability for repairable items: the mean number of life units during which all parts of an item perform within their specified limits during a particular measurement interval under stated conditions. mean time to failure (MTTF)—A basic measure of system reliability for nonrepairable items: the total number of life units for an item divided by the total number of failures within that population during a particular measurement interval under stated conditions.



mean time to repair (MTTR)—A basic measure of maintainability: the sum of corrective maintenance times at any specific level of repair divided by the total number of failures within an item repaired at that level during a particular interval under stated conditions. measurement—1. The process of evaluating a property or characteristic of an object and describing it with a numerical or nominal value. 2. A series of manipulations of physical objects or systems according to a defined protocol that results in a number. measurement process—Repeated application of a test method using a measuring system. measuring system—In general, the elements of a measuring system include the instrumentation, calibration standards, environmental influences, human operator limitations, and features of the workpiece or object being measured. median—A measure of central tendency that divides an ordered data set in half; 50 percent of the data lie at or below this value and 50 percent of the data lie above this value. milestone—A specific time when a critical event is to occur; a symbol placed on a milestone chart to locate the point when a critical event is to occur. milestone chart—Another name for a Gantt chart. minor defect—A defect that may cause difficulty in assembly or use of a product but will not prevent the product from being properly used nor pose any hazard to users. mistake—Similar to an error but with the implication that it could be prevented by better training or attention. mode—The value in a data set that occurs most often. There can be more than one mode for a sample. multi-voting—A decision-making tool that enables a group to sort through a long list of ideas to identify priorities. mutually exclusive events—Events that do not have outcomes in common or that do not occur jointly. Myers-Briggs Type Indicator—A method and instrument for assessing personality type based on Carl Jung’s theory of personality preferences.

N nominal group technique—A technique similar to brainstorming, used by teams to generate and make a selection from ideas on a particular subject. Team members are asked to silently come up with as many ideas as possible, writing them down. Each member is then asked to share one idea, which is recorded. After all the ideas are recorded, they are discussed and prioritized by the group.



nonconformity—A departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to meet a specification requirement. null hypothesis—In hypothesis testing, a statement about a population parameter or distributional form of a quality characteristic that is to be tested. It is often the statement of no difference.

O objective—A quantitative statement of future expectations and an indication of when the expectations should be achieved; it flows from goal(s) and clarifies what people must accomplish. objective evidence—Verifiable qualitative or quantitative observations, information, records, or statements of fact pertaining to the quality of an item or service or to the existence and implementation of a quality system element. observation—The process of determining the presence or absence of attributes or making measurements of a variable. Also, the result of the process of determining the presence or absence of attributes or making a measurement of a variable. observational study—Analysis of data collected from a process without imposing changes on the process. occurrence—The likelihood of a failure occurring. Occurrence is evaluated based on a 10-point scale. At the lowest end of the scale (1) it is assumed that the probability of a failure is unlikely. At the highest end of the scale (10) it is assumed that the probability of a failure is nearly inevitable. operating characteristic (OC) curve—For a sampling plan, the OC curve indicates the probability of accepting a lot based on the sample size to be taken and the fraction defective in the batch. organization—Company, corporation, firm, enterprise, or institution, or part thereof, whether incorporated or not, public or private, that has its own functions and administration. outlier—One or more observations that deviate significantly from the majority of the sample from which they came.

P p value—The probability of getting a value of the test statistic as extreme or more extreme than that observed if the null hypothesis is true. The p value is the actual or observed significance level for a test. parameter—A constant or coefficient that describes some characteristic of a population.



Pareto diagram—A graphical tool used to rank causes of problems from most significant to least significant. payback period—The number of years it will take the results of a project or capital investment to recover the investment from net cash flows. poka-yoke—A term that means to mistake-proof a process by building safeguards into the system that avoid or immediately find errors. The term comes from the Japanese terms poka, which means “error,” and yokeru, which means “to avoid.” policy—A high-level overall plan embracing the general goals and acceptable practices of a group. population—The totality of items or units of material under consideration. power—In statistical inference, it is the probability of rejecting a false null hypothesis. precision—The closeness of agreement between randomly selected individual measurements or test results. probability—A measure assigned to events in a sample space. It takes on values between zero and one inclusive and provides a measure of the likelihood that the event will occur. probability density function (pdf)—A function that describes the probability distribution of a continuous random variable. probability mass function (pmf)—A function that describes the probability distribution of a discrete random variable. process—An activity or group of activities that takes an input, adds value to it, and provides an output to an internal or external customer; a planned and repetitive sequence of steps by which a defined product or service is delivered. process capability—The ability or capability of a process to meet its intended purpose. It is a measure of how well the process produces outcomes that meet specifications. process improvement team (PIT)—A natural work group or cross-functional team whose responsibility is to achieve needed improvements in existing processes. The life span of the team is based on the completion of the team purpose and specific tasks. process mapping—The flowcharting of a work process in detail, including key measurements. producer’s risk (` )—For a sampling plan, refers to the probability of not accepting a lot the quality of which has a designated numerical value representing a level that is generally desirable. Usually the designated value will be the acceptable quality level. Also called alpha risk or probability of a type I error.



product identification—A means of marking parts with a label, etching, engraving, ink, or other means so that different part numbers and other key attributes can be identified. program evaluation and review technique (PERT)—An event-oriented project management planning and measurement technique that utilizes an arrow diagram or road map to identify all major project events and demonstrates the amount of time (critical path) needed to complete a project. It provides three time estimates: optimistic, most likely, and pessimistic. project lifecycle—A typical project lifecycle consists of five sequential phases in project management: concept, planning, design, implementation, and evaluation. project management—The entire process of managing activities and events involved throughout a project’s lifecycle. project plan—All the documents that comprise the details of why the project is to be initiated, what the project is to accomplish, when and where it is to be implemented, who will have responsibility, how implementation will be carried out, how much it will cost, what resources are required, and how the project’s progress and results will be measured.

Q qualitative variable—A variable whose possible outcomes are nonnumeric or categorical. quality assurance—All the planned or systematic actions necessary to provide adequate confidence that a product or service will satisfy given needs. quality audit—A systematic, independent examination and review to determine whether quality activities and related results comply with planned arrangements and whether these arrangements are implemented effectively and are suitable to achieve the objectives. quality audit observation—Statement of fact made during a quality audit and substantiated by objective evidence. quality auditor—Person qualified to perform quality audits. quality control—The operational techniques and the activities that sustain a quality of product or service that will satisfy given needs; also, the use of such techniques and activities. quality council—Sometimes referred to as a “quality steering committee.” The group driving the quality improvement effort and usually having oversight responsibility for the implementation and maintenance of the quality management system; it is operated in parallel with the normal operation of the business.



quality function deployment (QFD)—A structured method in which customer requirements are translated into appropriate technical requirements for each stage of product development and production. The QFD process is often referred to as listening to the voice of the customer. quality improvement—Actions taken throughout an organization to increase the effectiveness and efficiency of activities and processes in order to provide added benefits to both the organization and its customers. quality management—The totality of functions involved in organizing and leading the effort to determine and achieve quality. quality manual—A document stating the quality policy and describing the quality system of an organization. quality planning—The activity of establishing quality objectives and quality requirements. quality policy—Top management’s formally stated intentions and direction for the organization pertaining to quality. quality surveillance—Continual monitoring and verification of the status of an entity and analysis of records to ensure that specified requirements are being fulfilled. quality system—The organizational structure, procedures, processes, and resources needed to implement quality management. quantitative variable—A variable whose outcomes are numeric—continuous or discrete.

R random effect—In analysis of variance an effect is random if the factor levels included in the test or experiment are randomly selected from a larger population of possible levels. The results of the test conducted would then apply to the entire population of factor levels and not just those included in the experiment. random error—Error that occurs as a result of natural variation in a process or system. It is variation that occurs when taking repeated measurements on the same unit or item under identical conditions. Also referred to as experimental error. random experiment—An experiment that has more than one possible outcome. random sampling—The process of selecting units for a sample in such a manner that all combinations of units under consideration have an equal or ascertainable chance of being selected as the sample. random variable—A function that associates a real number to each outcome in an experiment.



range—The difference between the largest and smallest value or observation in a data set. It provides a measure of dispersion in a set of data. rational subgrouping—A method for collecting data that will allow for minimizing the chance of variability due to assignable causes while maximizing the chance of variability due to chance or natural causes. A fundamental and nontrivial concept in statistical process control. readability—Readability is the ease of reading the instrument scale when a dimension is being measured. record—A document or electronic medium that furnishes objective evidence of activities performed or results achieved. regression analysis—Statistical techniques for determining and modeling the relationship between a dependent variable and one or more independent variables. The response variable is also referred to as a response and the independent variables are also referred to as regressor or predictor variables. regressor variable—In regression analysis it is the independent variable. Also known as the predictor variable. reinforcement—The process of providing positive consequences when an individual is applying the correct knowledge and skills to the job. It has been described as “catching people doing things right and recognizing their behavior.” Caution: less than desired behavior can also be reinforced unintentionally. rejection region—In significance testing, the values of the test statistic that will lead to rejection of the null hypothesis. Sometimes referred to as the critical region. reliability—The probability that an item can perform its intended function for a specified interval under stated conditions. repeatability—How close the measurements of an instrument are to each other if such measurements are repeated on a part under the same measuring conditions. replication—The repetition of the set of all the treatment combinations to be compared in an experiment. Each of the repetitions is called a replicate. reproducibility—Reproducibility is a measure of the degree of agreement between two single test results made on the same object in two different, randomly selected measuring locations or laboratories. residual—The difference between the actual or observed response and the predicted response for the variable of interest. residual analysis—An examination of the residuals used to determine the adequacy of a fitted model. It is used to check the validity of assumptions made in model fitting.



resource requirements matrix—A tool to relate the resources required to the project tasks requiring them (used to indicate types of individuals needed, material needed, subcontractors, and so on). response variable—The variable that shows the observed results of an experimental treatment. It is the dependent variable in regression analysis. return on investment (ROI)—An umbrella term for a variety of ratios measuring an organization’s business performance, calculated by dividing some measure of return by a measure of investment and then multiplying by 100 to provide a percentage. In its most basic form, ROI indicates what remains from all money taken in after all expenses are paid. robust designs—Products or processes that continue to perform as intended in spite of manufacturing variation and extreme environmental conditions during use. robustness—The condition of a product or process design that remains relatively stable with a minimum of variation even though factors that influence operations or usage, such as environment and wear, are constantly changing.

S sample—A group of units, portions of material, or observations taken from a larger collection of units, quantity of material, or observations that serve to provide information that may be used as a basis for making a decision concerning the larger quantity. sample integrity—Samples are maintained in a unique manner to avoid corruption or confusion with others. sample space—The set of all possible outcomes of a random process or random experiment. sample standard deviation—A measure of dispersion for a set of observations. It is the positive square root of the sample variance. sample variance—A measure of dispersion for a set of observations. It provides a measure of the variability in a sample of data. sampling distribution—The probability distribution of a statistic calculated from a random sample of a given size. scatter diagram—A two-dimensional plot of data resulting from two random variables (bivariate data). The scatter diagram is a tool that can reveal associations between two variables. Also known as a scatter plot. scribe—The member of a team assigned the responsibility for recording minutes of meetings.



self-directed work team (SDWT)—A team that requires little supervision and manages itself and the day-to-day work it does; self-directed teams are responsible for whole work processes and schedules, with each individual performing multiple tasks. sensitivity—Sensitivity can be defined as the least perceptible change in dimension detected by the measuring instrument and shown by the indicator. severity—An indicator of the severity of a failure should a failure occur. Severity can be evaluated based on a 10-point scale. At the lowest end of the scale (1) it is assumed that a failure will have no noticeable effect. At the highest end of the scale (10) it is assumed that a failure will impact safe operation or violate compliance with regulatory mandate. Six Sigma approach—A quality philosophy; a collection of techniques and tools for use in reducing variation; a program of improvement that focuses on strong leadership tools and an emphasis on bottom-line financial results. special causes—Causes of variation that arise because of special circumstances. They are not an inherent part of a process. Special causes are also referred to as assignable causes. sponsor—A member of management who oversees, supports, and implements the efforts of a team or initiative. stable process—A process for which no special causes of variation are present. stages of team growth—The four development stages through which groups typically progress: forming, storming, norming, and performing. Knowledge of the stages helps team members accept the normal problems that occur on the path from forming a group to becoming a team. stakeholders—People, departments, and organizations that have an investment or interest in the success or actions taken by the organization. standard—A statement, specification, or quantity of material against which measured outputs from a process may be judged as acceptable or unacceptable. standard deviation—A measure of dispersion or spread. It is equal to the positive square root of the variance. standard error—The standard deviation of the sampling distribution of a statistic. In general, it is the standard deviation of any estimator of a parameter. standard normal random variable—A random variable that is normally distributed with mean zero and variance one. statement of work (SOW)—A description of the actual work to be accomplished. It is derived from the work breakdown structure and, when combined with the project specifications, becomes the basis for the contractual agreement on the project (also referred to as scope of work). statistic—A quantity calculated from a sample of observations, most often to form an estimate of some population parameter.



statistical control—A process is considered to be in a state of statistical control if no special causes are present. statistical process control (SPC)—The application of statistical techniques to control a process. stem-and-leaf plot—A graphical display using the actual observations. The “stem” consists of the leading digit(s) of the observation and the leaf consists of the next digit of the observation. steering committee—A group responsible for overall selection of continuous improvement projects. strategic planning—A process to set an organization’s long-range goals and identify the actions needed to reach the goals. substitute quality characteristic—A producer’s view/expression of what constitutes quality in a product or service. subsystem—A combination of sets, groups, and so on, that performs an operational function within a system and its major subdivision of the system. supply chain—The series of processes and/or organizations that are involved in producing and delivering a product to the final user. surface metrology—Surface metrology may be broadly defined as the measurement of the difference between what a surface actually is and what it is intended to be. It may involve other terms such as surface roughness and surface finish. SWOT analysis—An assessment of an organization’s key strengths, weaknesses, opportunities, and threats. It considers factors such as the organization’s industry, competitive position, functional areas, and management. system—A composite of equipment, skills, and techniques capable of performing or supporting an operational role, or both. A complete system includes all equipment, related facilities, material, software, services, and personnel required for its operation and support to the degree that it can be considered self-sufficient in its intended operating environment. systematic error—Error that remains the same over repeated measurements taken under assumed identical conditions.

T t distribution—The distribution of the ratio of two independent random variables. The random variable in the numerator is a standard normal random variable. The random variable in the denominator is the square root of a chisquare random variable divided by its degrees of freedom. Also known as Student’s t distribution. takt time—Time needed to produce a product in order to meet customer demand.



team—A set of two or more people who are equally accountable for the accomplishment of a purpose and specific performance goals; it is also defined as a small number of people with complementary skills who are committed to a common purpose. team building—The process of transforming a group of people into a team and developing the team to achieve its purpose. test statistic—A statistic used to test a given statistical hypothesis. It is a quantity calculated from a sample of data. testing—A means of determining the capability of an item to meet specified requirements by subjecting the item to a set of physical, chemical, environmental, or operating actions and conditions. timekeeper—A member of a team who monitors progress against a predefined schedule during meetings. tolerance interval—A statistical interval that contains a stated percentage of a population with a specified level of confidence. total sum of squares—The sum of the squared differences between each observation in a data set and the overall mean of the data set. traceability—The ability to trace the history, application, or location of an item or activity and like items or activities by means of recorded identification. traceability system—A formal set of procedures, usually implemented in a computerized database, that allows the manufacturer of a unit to trace it and its components back to the source. treatment—A combination of the levels of each of the factors assigned to an experimental unit. true quality characteristic—A customer’s view/expression of what constitutes quality in a product or service. t-test—A significance test that involves the t distribution. type I error—The incorrect decision that a process is unacceptable when, in fact, perfect information would reveal that it is located within the zone of acceptable processes. type II error—The incorrect decision that a process is acceptable when, in fact, perfect information would reveal that it is located within the zone of rejectable processes.

U unbiased estimator—An estimator whose expected value is equal to the parameter for which it is an estimator. uniform distribution—A distribution whose values are equally distributed over an interval. Each possible outcome is assigned equal probability. The uniform distribution is defined for both continuous and discrete random variables.



V value—The net difference between customer-perceived benefits and burdens, sometimes expressed as a ratio of benefits to burdens or a ratio of worth to cost. variables data—Data resulting from the measurement of a parameter or a variable. The resulting measurements may be recorded on a continuous scale. (Contrast with attributes data.) variance—A measure of dispersion. For a set of data, it is the sum of the squared differences between the individual observations and the mean of the observations. In general, it is the expected value of the squared difference between a random variable and its mean. variance components—In analysis of variance with random effects present, these are the variances associated with the random effects.

W work breakdown structure (WBS)—A project management technique by which a project is divided into tasks, subtasks, and units of work to be performed. work group—A group composed of people from one functional area who work together on a daily basis and whose goal is to manage and improve the processes of their function.





References

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Index

A

ANSI/ISO/ASQ QE 19011S-2004 standard, five purposes of quality audits under, 101 appraisal costs, 108 Aristotle, 60–61 arrow diagram, 304–8 assignable causes, of variation, 492 attribute data, 360 attribute gage R&R studies, 270–71 attributes control charts, 503–12 control limit formulas (Appendix B), 585 attributes data measurement of, 124–25 short-run SPC for, 524–26 audit planning and implementation, 102–3 quality, 100–104 reporting and follow-up, 103–4 versus survey and sampling inspection, 73–74 autocorrelation, in time-series analysis, 489–90 automatic gauging, 362 Automotive Industry Action Group (AIAG), rules for statistical control, 520 availability, and maintainability, 150–51 average outgoing quality (AOQ), 199, 200 for double and multiple sampling plans, 208 average outgoing quality limit (AOQL), 199, 200 for double and multiple sampling plans, 208 average quality protection, versus lot-by-lot sampling, 195 average run length (ARL), 521 average sample number (ASN), 208–10

acceptable quality limit (AQL), 198 under ANSI/ASQ Z1.4-2003, 210 under ANSI/ASQ Z1.9-2003, 216 acceptance number, 200–203 acceptance sampling, 74, 193–226 by attributes, 198 purposes of, 193–94 accuracy, in measurement, 252, 257–58 action plan, 22 active listening, 61–62 activity network diagram (AND), 29, 304–8 ADDIE model, of quality training, 117, 119 adult learners, and quality training, 121 affinity diagrams, 290–92 alternative hypothesis (Ha), 431 American Society for Quality (ASQ), 3 Certified Quality Engineer (CQE) Body of Knowledge (Appendix A), 576–84 Code of Ethics for Professional Conduct, 41–44 American Society for Quality Control (ASQC), 3 analysis of experiments, 534–74, 542 analysis of variance (ANOVA), 459–69 approach to DOE, 558–60 general notation and methods, 460–61 in linear regression, 481–82 for one-factor experiments, in DOE, 543 one-way, 459–60 table, 466–69 table, for DOE, 546–47 analysis of variance method, of GR&R study, 260–61, 265–66, 270 analytical hierarchy process (AHP), 303–4 analytical studies, 381 AND gate, 176, 296 angle measurements, 231 angular measuring devices, 232–33 ANSI/ASQ Z1.4-2003 standard, 198, 210–11, 213–214, 215 ANSI/ASQ Z1.9-2003 standard, 216–17 ANSI/ASQC Z1.4-1993 standard, 198, 210

B balanced scorecard, 20 base units, definitions of, 247–49 batch control, 224–25

655


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. 656 Index

bathtub curve, 151–54 Bell Telephone System, 4 benchmarking, 16–17, 313–16 bias in measurement, 257–58 in point estimation, 418–19 bilateral tolerance, 131 binomial distribution, 146–47, 408–10 table (Appendix J), 605–6 bivariate normal distribution, 404–5 blocking, in DOE, 541, 543–44 importance of, 548–49 box plots, 373–74 box-and-whisker diagrams, 373–74 brainstorming, 55–56 breakthrough thinking, 315–16 Brumbaugh, Martin, 3 business strategy, Six Sigma as, 324–25

C c control chart, 508–10 CADDIEM model, of quality training, 117 calibration control system, 254–55 of measuring systems, 253–55 standards, 246 Canadian Standards Association (CSA), 340 capability study, 380 categorical frequency distribution, 370–71 Cauchy distribution, 407 causal relationships, in linear regression, 488 cause-and-effect diagrams, 278–81 center, choosing, for measurement, 238 central limit theorem, 372, 415–17 central tendency, measures of, 365–67 Cequent Performance Products, 40 Certified Quality Engineer (CQE) Body of Knowledge (Appendix A), 576–84 change lack of plan for, as obstacle to quality, 81 sustaining and communicating, 332–33 change control, 225 check sheets, 281–82, 362 checklist manufacturing/quality, 75 supplier procurement, 75–76 chi-square distribution, 406 table (Appendix K), 607–8 Code of Ethics for Professional Conduct, ASQ, 41–44 coefficient of determination, in linear regression, 486 coefficients, in linear regression, 475 common causes, of variation, 492

communication, poor, as obstacle to quality, 80 communication process, 60–63 communication skills, 59–63 need for, 59 community, as stakeholder, 18 comparators, 234 complement, of event, 383 concurrent engineering, 130 conditional probability, 386–87 and independence, 388–90 confidence intervals, 419–30 interpretation, 420–21 large-sample, on m, 426–27 on m1–m2, 440–41 on m1–m2, variance unknown but assumed equal, 443–44 on population proportion p, 429–30 on population standard deviation s, 429 on population variance s 2, 428–29 in simple linear regression, 480–81 for a single population mean m, 421–26 for two population means m1, m2, 438–39 for two population proportions p1, p2, 444–46 for two population variances s 12, s 22, 446–49 configuration control, 225–26 conflict resolution, in teams, 56–58 confounding, in DOE, 540–41, 568 consensus, in teams, 57–58 consistency, in measurement, 252 constant failure rate, 152 constants, for control charts (Appendix C), 586 consumer’s risk (b ), 199 containment, in reaction plan, 81 contingency tables, 384–86, 469–72 continuous data, 360 continuous distributions, 394–407 summary of, 407 continuous flow manufacturing, 336–37 continuous improvement, 273–357 techniques, 318–45 continuous sampling plans, 222–24 continuous uniform distribution, 403–4 contour plot, 563–64 control charts analysis of, 519–21 choosing, 518 constants for (Appendix C), 586 in GR&R studies, 267–69 for nonconformities, 507–8 in SPC, 494–518 in time-series analysis, 489 control factors, in DOE, 535


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Index 657

control limits, 494–95, 530 formulas (Appendix B), 585 control plans, 180–82 example, 183–84 coordinate measuring machines (CMMs), 238–40 classification, 240–42 corrective action, 88, 171–72 seven phases of, 346–54 corrective maintenance, 150 correlation in linear regression, 483–88 in scatter diagrams, 284–85 cost of quality, 105–15 Cp, process capability index, 530–31 Cpk, process capability index, 531–32 Cpm, process capability index, 532 Cr, process capability index, 531 Crawford slip method, of brainstorming, 55–56 creative thinking, in quality improvement, 316 creativity, 351 lost, as waste, 340 critical defect, 190 under ANSI/ASQ Z1.4-2003, 211 critical path method (CPM), 13, 29, 304–8 critical value, in hypothesis testing, 449 Crosby, Philip, 6, 8 cumulative distribution function (cdf), 396 estimating, 140–42, 144 of failure, 139 cumulative frequency distribution, 370 cumulative sum (CUSUM) control charts, 512–15 patterns and trends on, 516–17 tabular form of, 513–15 V-mask format of, 515 customer focus lack of, as obstacle to quality, 82 in TQM, 319 customer needs and wants, 64–65 customer relations, 64–71 customer value analysis, 69 customer-driven quality, 70–71 customer-focused companies, 10 key characteristics of, 70–71 customers, as stakeholders, 18 cycle time reduction, 340–42

D dashboard, 20–21 data accuracy, 363–65

coding, 362–63 collecting and summarizing, 360–78 shape of, 369 types of, 360 data collection methods, 361–63 data errors, strategies to avoid, 364 data transformations, nonnormal, 533 decision making statistical, 418–72 team, 55–58 decision tree, example, 25–26 defect, definition, 190 defect correction, as waste, 339 defects, classification of, 190–91 defensiveness, in communication process, 63 defining relation, in fractional factorial designs, 568–71 degrees of freedom, in ANOVA, 462–63 delivery terms, 79 Deming, W. Edwards, 3, 4 14 points, 4, 5 dependent variable, 475 in DOE, 534, 536 deployment techniques, 14–33 design choosing, in DOE, 539–40 definition, 126 design concepts, translating design inputs into, 129–30 design FMEA, 163–71, 181 detection criteria, 170 example, 173 occurrence criteria, 169 severity criteria, 167 design for cost, 130 design for manufacturability, 130 design for reliability, 130 design for Six Sigma, 130 design inputs, 129–30 design of experiments (DOE), 534–74 analysis of results, 542 design principles, 540–42 objectives, 539 and statistical control, 574 terminology, 534–38 design principles, in DOE, 540–42 design resolution, in DOE, 568–71 design review, 128–29 design verification, 135–36 destructive tests, 242–44 diagnosis, in reaction plan, 181–82 dial indicators, 234 dimensional inspection, 229–30 dimensional measurements, instrumentation for, 231–32 dimensioning, of technical drawings, 131–34



discrete data, 360 discrete distributions, 408–13 disjoint events, in probability, 390 disposition, in reaction plan, 182 distributions graphical methods for depicting, 374–78 shapes of, 369 types of, 370–72 DMAIC methodology, 313, 333–34 document control, four requirements for, 91–92 documentation, of quality system, 90–92 DOD-STD-480A standard, 226 DOD-STD-481 standard, 226 Dodge-Romig tables, 215 Dodge’s continuous sampling plans, 222–23 double sampling plans, 205–7, 213–14

E economics of quality, 105–7 eddy current testing, 244 Edwards, George, 3 efficiency in DOE, 541–42 in point estimation, 419 employee empowerment lack of, as obstacle to quality, 81 in TQM, 319 employee motivation, lack of, as obstacle to quality, 81 employees, as stakeholders, 18 environmental stress screening (ESS), 156–57 Environmental Stress Screening of Electronic Hardware (ESSEH), Institute of Environmental Sciences, 156 equipment, for teams, 48 error, in design of experiments, 536 error, in measurement, 250–51 measurement system, 257 sources of, 250 error sum of squares, 461–62 in DOE, 546 in linear regression, 476–77, 481 error-proofing, principles, 356 errors common causes of, 363 strategies to avoid, 364 estimated effects, in DOE, 554, 556–57 ethics, code of, ASQ, 41–44 ethos, rhetorical process, 61 evaluation, supplier, 77–79 event, definition, 382 independent, 388

executive management, as stakeholders, 17 expected frequency, in goodness-of-fit tests, 457 expected value, 392–93 of binomial distribution, 410 of continuous distribution, 414 of continuous uniform distribution, 404 of discrete distribution, 414 of hypergeometric distribution, 412 of Poisson distribution, 411 and variance, 400–401 of Weibull distribution, 402–3 experiment, definition, 381 experimental error, in DOE, 536, 537 experiments design and analysis of, 534–74 general model and notation, 544–46 one-factor, in DOE, 542–49 planning and organizing, 538–40 explanatory variable, 475 exponential distribution, 400 table (Appendix L), 609–10 exponentially weighted moving average (EWMA) control charts, 515–16 patterns and trends on, 516–17 external failure costs, 108 external quality audit, 100

F F distribution, 407 in ANOVA, 463 F0.01 (Appendix I), 601–4 F0.05 (Appendix H), 597–600 F0.10 (Appendix G), 593–96 facilitation, team principles and techniques, 52–58 purposes of, 52–53 facilitator, team intervention methods, 54–55 roles and responsibilities, 52–55 skills required, 54 factorial designs, in DOE, 535–36 versus randomized block designs, 551 two-level, 551–55 factorial experiment, 465–66 factors, in design of experiments, 535–36 choosing, 539 determining optimal settings of, 563–64 fail-safe devices, 355 failure costs, 108 failure density function, estimating, 140–42, 144 failure mode and effects analysis (FMEA) 157, 161, 162–72, 295



inputs to, 159 and other quality tools, 159–60 outputs from, 160 planning for, 158 standards for, 157 team members, 158–59 failure mode effects and criticality analysis (FMECA), 172–75 basic steps, 160–61 example, 175 types, 162–63 failure models, miscellaneous, 152–54 failure time distribution, 138–39 fault tree analysis (FTA), 176–77 feedback, in communication process, 62–63 Feigenbaum, Armand, 6 financial results, short-term focus on, as obstacle to quality, 82 fishbone diagram, 278 fitted regression line, 475, 476–77 5S system, 7, 320 fixed significance level approach, to hypothesis testing, 449 flowchart, 275–76 fraction nonconforming, 124 fractional factorial designs, in DOE, 566 higher-level, 567–68 two-level, 567–74 frequency distributions, 370–72 full-factorial experiments, 536–37, 549–66

G gage blocks, 234 gage repeatability and reproducibility (GR&R), 181 control charts in, 267–69 issues and considerations in, 269 studies, 258–71 attribute, 270–71 results, comparison of 266–67 gages, 233 Gantt chart, 29, 30 gauging automatic, 362 versus testing, 243 general addition rule, in probability, 384, 390 general failure rate model. See bathtub curve general multiplication rule, in probability, 387–88, 390 geometric dimensioning and tolerancing (GD&T), 132 geometric distribution, 412–13 goals, deployment, 15–16 Goldratt, Eliyahu, 6

go/no-go gages, 125, 233 goodness-of-fit tests, 456–59 graphical methods for depicting distributions, 374–78 for depicting relationships, 372–74 grouped data, 139–44 grouped frequency distribution, 370 Gryna, Frank, 116, 276

H hazard assessment tools, 156–77 hazard rate function, 138, 139, 140–41, 144 histograms, 282–84 house of quality, in QFD, 66 hypergeometric distribution, 411–12 hypothesis testing, 430–52 for independence, 470–72 on m1–m2 with population variances known, 439–40 on m1–m2 with population variances unknown, 441–43 p value approach to, 449–51 on population proportion p, 437–38 on population variance s 2, 434–36 in simple linear regression, 478–81 for single population mean m, 432–34 for two population means m1, m2, 438–39 for two population proportions p1, p2, 446 for two population variances s 12, s 22, 446–49

I idea processing, in teams, 55–58 incoming inspection, 74 INCOTERMS, delivery terms, 79 independence hypothesis test for, 470–72 in probability, 388–90 independent variable, 475 in DOE, 535, 536 individuals control charts, 500–502 inner/outer array designs, in DOE, 572–73 inspection incoming, 74 skip-lot, 74 source, 74 versus testing, 242–43 inspection, measuring, and test equipment, control of, 88 instantaneous failure rate, 138 interaction effects, in DOE, 556–57 interactions, in DOE, 541



intercept, in linear regression, 475 internal failure costs, 108 internal quality audit, 100 internal rate of return (IRR), 24–26 International Bureau of Weights and Measures, 246 international standards, versus national standards, 73 interquartile range, 373 interrelationship digraphs, 292–94 intersection, of events, 382 interval scales, 361 inventory, as waste, 338–39 Ishikawa, Kaoru, 6, 274 Ishikawa diagram, 278 ISO 8402 standard, 8 ISO 9000, family of standards, 94–98 changes from ISO 9000:1994 to ISO 9000: 2000, 94–95 eight quality management principles, 94 ISO 9000:2005 Fundamentals and vocabulary, 95 ISO 9001:2008 Requirements, 96–97 ISO 9004:2000 Guidelines for performance improvements, 97–98 ISO/R468 standard, 235

J Japan, role in quality movement, 3, 4 Juran, Joseph M., 3, 4–6, 8, 14, 276 Juran trilogy, 4–5 just-in-time inventory, 7

K kaizen, 6, 319–20, 341 kaizen blitz, 6, 341 kanban system, 342 Kaplan, Robert, 20 k-out-of-n systems, and reliability, 146–47 k-values (statistical tolerance factors) (Appendix D), 587

L Latin square designs, in DOE, 551 layout devices, 233 leadership, 1–83 lack of, as obstacle to quality, 82 principles and techniques, 45–51 in TQM, 319 lean enterprise, 334–45

philosophy, 7 tools, 342–44 least squares circle (LSC), 238 least squares method, of linear regression, 475–76 legal measurements, 246 length measurements, 231 Lennox Industries, 187 levels, in design of experiments, 535, 536 levels of inspection, under ANSI/ASQ Z1.42003, 211–13 limit gages, 125 linear correlation, simple, 483–88 linear measuring instruments, basic, 232 linear regression, 473–83 analysis, assumptions in, 482–83 multiple, 486–88 notation and definitions, 474–75 simple, hypothesis testing in, 478–81 linear responsibility matrix (LRM), 29 linearity, in measurement, 258 liquid penetration testing, 244 local control of error, in DOE, 541 locating devices, 233 lognormal distribution, 405 logos, rhetorical process, 61 lot size, 200–203 lot tolerance percent defective (LTPD), 198 lot-by-lot sampling, versus average quality protection, 195

M magnetic particle testing, 244 maintainability and availability, 150–51 and reliability, 137–77 maintenance productivity metric, 19 maintenance tools, predictive and preventive, 137–44 major defect, 190 under ANSI/ASQ Z1.4-2003, 211 Malcolm Baldrige National Quality Award (MBNQA), 98–99 criteria, 99 management, 1–83 team sponsor, 48–49 marketing, quality in, 86–87 mass customization, 336 material control, 185–92 material identification, and status, 186–87 material review board (MRB), 191–92 material segregation, 189–90 material traceability, 187–89 matrix diagrams, 299–303



maximum inscribed circle (MIC), 238 maximum material condition (MMC), 132, 134 Mayo, Elton, 2 mean, 365–66 mean squares, in ANOVA, 466–69 mean time between failures (MTBF), 149, 150 mean time to failure (MTTF), 149–50 measure, resistant, 367 measurement error in, 250–51 scales, 361 standards of, 246 and test, 227–44 measurement assurance, 253 measurement methods, in DOE, 539 measurement process, 227 factors affecting, 250 operator fallibility in, 250–51 measurement system analysis (MSA), 256–71 nonmanufacturing applications, 271 terms and definitions, 256–58 measurement system error, 257 measurement technology, 231 measurement tools, 228–42 measurements context of, 245–49 length and angle, 231 measures of central tendency, 365–67 measures of variability, 368–69 measuring devices, angular, 232–33 measuring instruments linear, 232 selection of, 230 measuring system, 228 median, 366–67 median ranks, table (Appendix N), 613–14 metrics, of Six Sigma, 327–32 metrology, 245–55 concepts in, 250–52 standards, 246 MIL-STD-785B10 military standard, 135–36 MIL-STD-1235B military standard, 223–24 MIL-STD-1629A military standard, 157, 172 minimum circumscribed circle (MCC), 238 minimum radial separation (MRS), 238 minimum zone circle (MZC), 238 Minitab, rules for statistical control, 520 minor defect, 191 under ANSI/ASQ Z1.4-2003, 211 Minuteman Launch Control System, 176 mode, 367 model-fitting, in DOE, 560–61 monitoring, supplier, 78–79 motion, excess, as waste, 337 motivation, lack of, as obstacle to quality, 81

movement of material, excess, as waste, 339 moving average (MA) control charts, 518 moving average smoothing, in time-series analysis, 490 moving range, 498–502 muda, 319, 337–40 multinomial distribution, 412 multiple linear regression, 486–88 multiple sampling plans, 207, 214–15 multivoting, 56 mutually exclusive events, 383, 390

N National Institute of Standards and Technology (NIST), 246, 253 national standards, 246 versus international standards, 93 natural tolerance limits, 530 natural variation, 492 needs analysis, in quality training, 118 negative binomial distribution, 412 negative correlation, in linear regression, 484 nested design, in GR&R studies, 270 net present value (NPV), 24, 26 example, 27 noise factors, in DOE, 535, 536 nominal group technique, 56 nominal scales, 361 nonconforming product, control of, 88 nonconformities, control charts for, 507–8 nonconformity, definition, 190 nondestructive tests, 242–44 techniques, 243 nonnormal data transformations, 533 non-value-added activities, 337–40 normal distribution, 396–97 bivariate, 404–5 standard, 397–99 normal probability plot, 376 normal scores, table (Appendix O), 615–16 Norton, David, 20 np control chart, 506–7 nuisance factors, in DOE, 541 null hypothesis (H0), 431 numeric studies, 380

O objectives, deployment, 15–16 observed frequency, in goodness-of-fit tests, 457 Occupational Safety and Health Administration (OSHA), 43



on-condition maintenance, 150 one-factor experiments, 542–49 general model and notation, 544–46 100 percent inspection, versus sampling inspection, 194 one-way ANOVA, 459–60 operating characteristic (OC) curve, 195–96, 201, 202 for double sampling plan, 206–7 plotting, 196–98 operator fallibility, in measuring process, 250–51 OR gate, 176, 295 ordinal scales, 361 overadjustment, 492 overcontrol, 492 overproduction, 337

P p control chart, 504–6 paired-comparison tests, 453–56 paper standards, 249 parallel systems, and reliability, 145–46, 147, 148 parameter definition, 379 estimation, 418 parameters b 0 and b1, estimating, in linear regression, 475–76 Pareto, Vilfredo, 276 Pareto chart, 276–78 partnering, with suppliers, 79 part-to-part variability, estimating, 260 Pascal distribution, 412–13 pathos, rhetorical process, 61 payback period, 24, 26 performance capability, 527–33 performance measurement guidelines for, 19–20 tools, 18–19 personnel, as element of quality system, 89 physical artifacts, 249 plan–do–check–act (PDCA) cycle, 320 plan–do–study–act (PDSA) cycle, 320, 346 planning, 21–22 point estimation, 418–19 Poisson distribution, 400, 410–11 table (Appendix M), 611–12 poka-yoke, 342–43, 355–56 policy deployment, 15 politics, organizational, as obstacle to quality, 81 pooled variance, 441 population, definition, 379

population correlation coefficient (r), 484–85 population mean, 366 population standard deviation, 369 population variance, 368 positional tolerances, 133–34 positive correlation, in linear regression, 484 postproduction activities, as element of quality system, 88 power, in hypothesis testing, 452 Pp, process performance index, 532–33 Ppk, process performance index, 532–33 practical significance, versus statistical significance, 451 precision, in measurement, 252, 258 pre-control charts, 522–24 predicted value, in linear regression, 475 prediction of observations, in linear regression, 483 predictive maintenance, 150, 151 tools, 137–44 prevention costs, 108 preventive action, 355–57 preventive maintenance, 150–51 tools, 137–44 probability, 383 definition, 379 rules, 383, 390 terms and concepts, 381–93 probability density function, 394–96 probability distributions, 394–417 probability plots, 375–78 process, control of, as element of quality system, 88 process capability, 527–33 for nonnormal data, 533 short-term versus long-term, 533 process capability indices, 530–32 process capability studies, 527–29 process control, 179–271 acceptance sampling, 193–226 material control, 185–92 measurement and test, 227–44 measurement system analysis (MSA), 256–71 metrology, 245–55 tools, 180–84 process decision program charts (PDPC), 298–99 process design, 123–78 process development lifecycle, 126–28 process FMEA, 163–71, 181 detection criteria, 171 example, 174 occurrence criteria, 169 severity criteria, 168 process improvement team, 46



process log, 519 process maps, 308–10 process performance calculating, versus specifications, 529–30 indices, 532–33 process stability, 528 process value chain (PVC) diagrams, 311 processes, control of, as element of quality system, 88 processing, excess, as waste, 339–40 procurement standards and specifications, 72–73 producer’s risk (a ), 199 product control, 179–271 acceptance sampling, 193–226 material control, 185–92 measurement and test, 227–44 measurement system analysis (MSA), 256–71 metrology, 245–55 tools, 180–84 product design, 123–78 product development lifecycle, 126–28 product quality, controlling, 228–29 product safety, as element of quality system, 89 product verification, 88 programmable logic controller (PLC) systems, 35, 36 project documentation, 32–33 project estimation, 29–32 project evaluation and review technique (PERT), 29 project justification, tools, 22–32 project management, 22–33 project monitoring and measurement, 32 project planning, 29–32 sequence, 28–29 project prioritization, tools, 22–24 project team formation, 28 pull system, 336–37 purchasing, quality in, 87 push system, 336 p-values, in ANOVA, 463–65

Q qualitative factors, in DOE, 535 quality concept of, 289–90 cost of, 105–15 definition, lack of, as obstacle to quality, 82 definitions, 7–9

economics of, 105–7 foundations of, 2–9 history of, 2–7 implementing, 12 obstacles to, 80–82 quality audits, 100–104 planning and implementation, 102–3 reporting and follow-up, 103–4 roles and responsibilities in, 101–2 types of, 100–101 quality characteristics, classification of, 124–25 quality control tools, 274–88 quality cost system goal of, 107 implementation, 108–11 quality costs, 106 categories, 108 collection, 111–12 management of, 107–8 principles and lessons, 114–15 reducing, and quality improvement, 114 reporting, 113 summary and analysis, 112–13 using, 113 quality function deployment (QFD), 65–69 application of, 67 benefits of, 67 definitions and concepts, 65–66 in product and process design, 130 quality improvement barriers to, overcoming, 80–83 optimizing, 318–19 and reducing quality costs, 114 quality information system, 33–40 internally developed, example, 38–39 Web-based, example, 40 quality initiatives, leading, 50–51 quality management system, 10–40 concept and objectives, 33–34 strategy and tactics, 35–36 uses of, 34–35 quality management tools, 289–317 quality manual, 90–92 four tiers of, 90–91 quality philosophies, 2–9 quality planning tools, 289–317 quality records, as element of quality system, 89 quality standards, 93–99 miscellaneous, 98 national versus international, 93 selected national and international (Appendix Q), 619–21 quality system, 85–122 documentation of, 90–92



elements of, 86–89 quality training, 116–22 determining effectiveness of, 119–21 developing a program, 117–18 developing curricula and materials, 118–19 reasons for failure, 120–21 quantitative concepts, 379–93 terminology, 379–80 quantitative factors, in DOE, 535 quantitative methods and tools, 359–574 quartiles, in box plots, 373 quick fix, view of quality program, as obstacle to quality, 82

R radio frequency identification (RFID), 186 random error term, 475 random errors, 250, 256 random experiment, 381 random sampling, 364 random variable, 391 and binomial distribution, 409 probability density function of, 395–96 standard normal, 397–99 randomization in DOE, 540–41 in GR&R studies, 269 randomized block designs, in DOE, 543–44 versus factorial designs, 551 range method, of GR&R study, 259–60 rapid continuous improvement (RCI), 341 rating, supplier, 77–79 ratio scales, 361 rational subgrouping, in SPC, 493 reaction plan, 181 reactive customer-driven quality (RCDQ), 70 readability, in measurement, 252 reengineering, 320–21 regression analysis assumptions in, 482–83 in DOE, 560–61 regression coefficients, 475 regression sum of squares, 481 rejection region, in hypothesis testing, 431 relationships, graphical methods for depicting, 372–74 reliability defining and estimating, 138–39, 140 definition, 137 and maintainability, 137–77 reliability allocation, 18–19 reliability assessment tools, 156–77 reliability failure analysis and reporting, 154

reliability function, 138–39, 140–42, 144 repeatability, in measurement, 258 estimating, 260 replicates, in ANOVA, 469 replication in DOE, 536 in GR&R studies, 269 report, supplier survey, 76–77 reproducibility, in measurement, 258 estimating, 259–60 residual, in linear regression, 476 residual analysis, in DOE, 561–62 resistant measure, 367 resolution III designs, in DOE, 570 resolution IV designs, in DOE, 570–71 resolution V designs, in DOE, 571 resource requirements matrix (RRM), 29, 32 responses, in DOE, 539, 556 restricted randomization, in GR&R studies, 269 rhetoric, in communication, 60–61 risk priority number (RPN) obtaining, 161 taking action based on, 161–62 robust design, 356–57 robustness in analytical studies, 381 Taguchi concepts, in DOE, 571–73 roundness, measurement of, 237 methods, 237–38 run charts, 285–88 in time-series analysis, 489–90

S SAE J1739 standard, 157 safety assessment tools, 156–77 sample, definition, 379 sample autocorrelation, in time-series analysis, 489–90 sample homogeneity, 364–65 sample integrity, 224–26 sample mean, 365 sampling distribution of, 391–92 sample mode, 367 sample range, 368 sample size, 200–203 determining, in point estimation, 423–24 sample spaces, 381–82 sample standard deviation, 369 sample variance, 368 sampling random, 364 stratified, 364 types of, 213–15



sampling concepts, 195–203 sampling distribution of the sample mean, 391–92 sampling distributions, 405–7 sampling inspection versus audit and survey, 73–74 versus 100 percent inspection, 194 sampling plans, 205–24 sampling standards, 205–24 Sauer Danfoss Company, 186–87 scatter diagrams, 284–85 scheduling, 21–22 scientific measurements, 246 self-directed work team (SDWT), 47 sensitivity of hypothesis test, 452 in measurement, 252 sequential sampling plans, 219–24 series systems, and reliability, 145 serious defect, 190 service delivery FMEA, 163 set operations, 382–83 seven basic quality control tools, 274–75 shape of data, 369 shared vision, creating, 59–60 Shewhart, Walter, 2, 3, 320, 494 Shingo, Shigeo, 343–44 short-run SPC, 524–26 sigma–quality level metric, 327–31 significance level (a ), in hypothesis testing, 431, 452 simple linear correlation, 483–88 simple linear regression hypothesis testing in, 478–81 model, 474–75 single minute exchange of dies (SMED), 343–44 single sampling plans, 205, 213 SIPOC diagrams, 312–13 Six Sigma (methodology), 7, 321–32 as a business strategy, 324–25 design for, 130 implementing, 325–27 metrics of, 327–32 needs assessment, 322–23 skip-lot inspection, 74 slope, in linear regression, 475 SMART goals and objectives, 12–13 source inspection, 74 sparsity-of-effects principle, in DOE, 564 special addition rule, in probability, 383, 390 special causes, of variation, 492 special multiplication rule, in probability, 390 specification and design, quality in, 87 specification limits, 530 specifications

calculating process performance versus, 529–30 procurement, 72–73 technical, 131–34 spread, measures of, 368–69 stability, in measurement, 258 stakeholder(s), 28 identification and analysis, 17–18 standard deviation, 369 standard error in point estimation, 419 in simple linear regression, 478 standard experiment, in GR&R study, 261–62 standard normal distribution, 397–99 areas under, to left of Z-values (Appendix F), 590–92 for selected Z-values (Appendix E), 588–89 standard normal random variable, 397–99 standard repair, 191–92 standard work, 343 standards measurement, 246 procurement, 72–73 three components of, 247 standby systems, and reliability, 148–50 Starlink seed corn, contamination incident, 187 statistic, definition, 379 statistical conclusions, drawing, 380–81 statistical control, 519–20 and designed experiments, 574 rules for determining, 520–21 statistical decision making, 418–72 statistical methods, use of, as element of quality system, 89 statistical process control (SPC), 491–526 objectives and benefits, 491–92 short-run, 524–26 statistical significance, versus practical significance, 451 statistical significance of effects, testing for, in DOE, 558 statistical tolerance factors, for at least 99 percent of population (Appendix D), 587 statistical tolerance intervals, 427 statistics, descriptive, 365–72 stem and leaf plot, 372–73 stockholders, as stakeholders, 17 strategic planning, 10–14 strategic quality planning, 70 stratified sampling, 364 Student’s t distribution, 406–7, 438 sum of squares due to different factor levels, in DOE, 545



sum of squares due to different levels of blocking factor, in DOE, 545–46 sums of squares, in ANOVA, 461–63, 466–69 supervisory control and data acquisition (SCADA) systems, 35 supplier management, 72–79 suppliers monitoring, 78–79 partnering with, 79 rating and evaluation, 77–79 as stakeholders, 18 surveying, 74–77 surface, numerical assessment of, 234–37 surface roughness, parameters of, 236 surface texture measurement, 234 survey versus audit and sampling inspection, 73–74 supplier, 74–77 SWOT analysis, 10–11 system FMEA, 163 system reliability and maintainability indices, 144–51 systematic errors, 250, 256

T t distribution Student’s, 406–7 values of, table (Appendix P), 617–18 tabular method, of GR&R study, 259–60, 270 Taguchi, Genichi, 6 Taguchi robustness concepts, in DOE, 571–73 takt time, 341–42 tally sheet, 362, 281–82 Taylor, Frederick, 2 teams conflict resolution in, 56–58 developing, building, and organizing, 46–50 development, stages, 49–50 facilitation principles and techniques, 52–58 idea processing and decision making, 55–58 need for, 46 selecting members, 48 support mechanisms, 48–49 types of, 46–48 technical drawings, 131–34 technical measurements, 245 test, and measurement, 227–44 test statistic, in hypothesis testing, 431–32 testing versus gauging, 243

versus inspection, 242–43 theory of constraints, 6–7, 334 time, lack of, as obstacle to quality, 80 time-series analysis, 488–90 tolerance, definition, 131 and process capability, 530–31 tolerancing, 131–34 tools lean, 342–44 measurement, 228–42 product and process control, 180–84 quality control, 274–88 quality management and planning, 289–317 total productive maintenance (TPM), 344–45 total quality management (TQM), 319 traceability, in measurement, 252–53 traceability, material, 187–89 training program, developing, 117–18 quality, 116–22 of teams, 48 treatment combinations, in DOE, 535 treatments, in DOE, 535 tree diagrams, 294–96 trend analysis, in time-series analysis, 490 trust, lack of, as obstacle to quality, 81 turf issues, as obstacle to quality, 81 2k designs, in DOE half-fractions of, 567 with single replicate, 564–66 two-level factorial designs, in DOE, 551–55 two-way ANOVA, 465–66 two-way contingency table, 469 type I error, 199, 431 in hypothesis testing, 452 type II error, 199, 431 in hypothesis testing, 452

U u control chart, 510–12 ultrasonic testing, 244 ungrouped frequency distribution, 370 unilateral tolerance, 131 union, of events, 382 universal set, 381

V V model, of quality information system, 36–38 value stream mapping (VSM), 311–12 in lean enterprise, 334 variability, measures of, 368–69



variability unknown—standard deviation method, sampling plan, 217–19 variables correlation of, 483–84 relationships between, 473–90, 488 selection of, in SPC, 492–93 variables control charts, 495–500 control limit formulas (Appendix B), 585 variables data, 360 measurement of, 124 variables sampling plans, 215–19 variance of binomial distribution, 410 of continuous uniform distribution, 404 and expected value, 400–401 of hypergeometric distribution, 412 of Poisson distribution, 411 of Weibull distribution, 402–3 variance components, of GR&R study, 259 estimating, 262–66 variation, causes of, 492 verification design, 135–36 of preventive action, 356 in reaction plan, 182 visual factory, 342 visual inspection, 229 visual management, 7 V-mask, format of CUSUM chart, 515

W waiting, as waste, 338 Watson, H. A., 176 Weibull distribution, 154, 401–3 Weibull failure rate model, 154 Weibull probability plot, 377 work breakdown structure (WBS), 28, 30 work group, 47 work instructions, 182 work review meetings, 22

X x– and R control charts, 495–97 x– and s control charts, 497–500 Xerox Corporation, 16 X-ray testing, 244

Z Z-values areas under standard normal distribution to left of (Appendix F), 590–92 standard normal distribution for (Appendix E), 588–89



Certified Quality Engineer— Simulated Exam

THIS SECTION WILL NOT BE PERMITTED IN THE CLASSROOM WHILE SITTING FOR THE CERTIFICATION EXAMINATION. Eighty questions distributed by topic approximately proportional to that indicated in the CQE Body of Knowledge. (The CQE Exam allows five hours for 160 questions.) 1. The Juran trilogy for quality management consists of: A. planning, execution, reward. B. process, product, shipment. C. planning, control, improvement. D. production, perspective, payment. 2. A method of dealing with an inspector found to be falsifying the results of inspection of borderline product is to: A. criticize the inspector on the basis that the pattern of reading does not follow the laws of chance. B. review the procedure for evaluating and reporting borderline product. C. review the inspector’s results against the expected results calculated from a normal curve. D. criticize the inspector for not knowing how to read the inspection equipment. 3. The probability that defect type A occurs is .83, the probability that defect type B occurs is .83, and the probability that both occur is .83. Find the probability that at least one of the defects occurs. A. .83 B. .92 C. .33 D. 1.05 E. .029 1



Certified Quality Engineer—Simulated Exam

4. In the “storming” stage of team development: A. the team “storms” the threshold of the problem. B. team members think primarily as individuals. C. team members think primarily as team members. D. the team breaks up. 5. Deming estimated that ____% of quality problems are due to the system rather than the workers. A. 15 B. 50 C. 75 D. 85 6. The management team is establishing priorities to attack a serious quality problem. You are requested to establish a data collection system to direct this attack. You use which of the general management rules to support your recommendations as to the quantity of data required? I. You compare the incremental cost of additional data with the value of the information II. Your decision will correspond to the rules applicable for management decisions in other factors of production III. Your decision is based upon the pure relationship between value and cost A. I only B. I and II only C. I and III only D. I, II, and III 7. If prevention costs are increased to pay for engineering work in quality control, and this results in a reduction in the number of product defects, this yields a reduction in: A. appraisal costs. B. operating costs. C. quality costs. D. failure costs.


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Certified Quality Engineer—Simulated Exam

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8. The primary reason for evaluating and maintaining surveillance over a supplier’s quality program is to: A. perform product inspection at the source. B. eliminate incoming inspection costs. C. motivate suppliers to improve quality. D. make sure the supplier’s quality program is functioning effectively. 9. When planning a total quality system, one key objective is to provide a means of guaranteeing ”the maintenance of product integrity.” Which of the following quality systems provisions is designed to most directly provide such a guarantee? A. Drawing and print control B. Calibration and maintenance of test equipment C. Identification and segregation of nonconforming material D. Specification change control 10. The most desirable method of evaluating a supplier is: A. a history evaluation. B. a survey evaluation. C. a questionnaire. D. a discussion with the quality manager on the phone. 11. The advantage of a written procedure is: A. it provides flexibility in dealing with problems. B. unusual conditions are handled better. C. it is a perpetual coordination device. D. coordination with other departments is not required. 12. A quality control program is considered to be: A. a collection of quality control procedures and guidelines. B. a step-by-step listing of all quality control check points. C. a summary of company quality control policies. D. a system of activities to provide quality of products and service.




13. In recent months, several quality problems have resulted from apparent changes in design specifications by engineering, including material substitutions. This has only come to light through quality engineering’s failure analysis system. You recommend establishing which of the following quality system provisions as the best corrective action? A. A formal procedure for initial design review B. A formal procedure for specification change control (sometimes called an ECO or SCO system) C. A formal system for drawing and print control D. A formal material review board (MRB) system 14. One of the most important reasons for a checklist in an “in-process” audit is to: A. assure that the auditor is qualified. B. obtain relatively uniform audits. C. minimize the time required to complete the audit. D. notify the audited function prior to the audit. 15. ASQ certification is: A. licensure. B. peer recognition. C. registration. D. governmentally regulated. 16. Which line below is not found in the ASQ Code of Ethics? A. Will do whatever I can to promote the reliability and safety of all products that come within my jurisdiction B. Will not disclose information concerning the business affairs or technical processes of any present or former employer or client without his or her consent C. Will strive to improve each process I encounter D. Will take care that credit for the work of others is given to those to whom it is due 17. The primary purpose of audit working papers is to provide: A. evidence of analysis of internal control. B. support for the audit report. C. a basis for evaluating audit personnel. D. a guide for subsequent audits of the same areas.



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18. A quality engineer, when asked about details of a technical process used by his employer, is bound by the ASQ Code of Ethics to: A. obtain the consent of the employer first. B. divulge no information. C. inform his employer of the request. D. provide information verbally but provide no documents. 19. It is usually true that increasing the resources devoted to prevention efforts will: A. increase total cost of quality. B. decrease failure costs. C. increase quality department visibility. D. cause cost accounting miscalculations. 20. The acronym “AQL,” as used in sampling inspection, means: A. that level of lot quality for which there is a small risk of rejecting the lot. B. the average quality limit. C. the maximum percent defective that can be considered satisfactory as a process average. D. the quality level. 21. Which of the following elements is least necessary to a good corrective action feedback report? A. What caused the failure B. Who caused the failure C. What correction has been made D. When the correction is effective 22. A classification of characteristics makes it possible to: A. separate the “vital few” from the “trivial many” kinds of defects. B. direct the greatest inspection effort to the most important quality characteristics. C. establish inspection tolerances. D. allow the inspector to choose what to inspect and what not to inspect.




23. Where inspector efficiency is defined as the ratio of correct decisions to the total decisions regarding individual items, most inspection operations performed by human inspectors are approximately: A. 40–55% efficient. B. 55–70% efficient. C. 70–95% efficient. D. 95–100% efficient. 24. In a visual inspection situation, one of the best ways to minimize deterioration of the quality level is to: A. retrain the inspector frequently. B. have a program of frequent eye exams. C. add variety to the task. D. have a standard to compare against as an element of the operation. 25. The quality engineer should be concerned with the human factors of a new piece of in-house manufacturing equipment as well as its operational effects because it: I. may speed the line to the point where a visual operator inspection is impossible. II. may require the operator’s undivided attention at the controls so the product cannot be fully seen. III. may remove an operator formerly devoting some portion of time to inspection. A. I only B. II only C. I and III only D. I, II, and III 26. The technique of seeking out and studying the best products and processes in other divisions or companies with the intent of continuous improvement is called: A. quality function deployment. B. benchmarking. C. using Baldrige criteria. D. quality auditing.



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27. In ANSI/ASQ Z1.4-2003, the AQL is always determined at what probability of acceptance (Pa) on the OC curve? A. 0.05 B. 0.10 C. 0.90 D. 0.95 E. none of the above 28. In comparison with attributes sampling plans, variables sampling plans: A. have the advantage of greater simplicity. B. usually require a larger sample size for comparable assurance as to the correctness of decisions in judging a single quality characteristic. C. have the advantage of being applicable to either single or multiple quality characteristics. D. provide greater assurance, for the same sample size, as to the correctness of decisions in judging a single quality characteristic. 29. An operation requires shipments from your vendor of small lots of fixed size. The attribute sampling plan used for receiving inspection should have its OC curve developed using: A. the binomial distribution. B. the Gaussian (normal) distribution. C. the Poisson distribution. D. the hypergeometric distribution. 30. Using ANSI/ASQ Z1.4-2003, determine the sample size(s) for a lot of 300, AQL = 1.0%, level II normal inspection, double sampling. A. 50, 50 B. 20, 20 C. 32, 32 D. 80, 100 31. Precontrol starts a process specifically centered between: A. process limits. B. safety lines. C. normal distribution limits. D. three-sigma limits. E. specification limits.




32. Lots of 75 parts each are inspected to an AQL of 0.25% using normal inspection, level II single sampling. Assuming general inspection level II, what is the sample size for ANSI/ASQ Z1.4-2003? A. 13 B. 32 C. 50 D. 75 33. The cost of equipment used for inspection is allocated to which category? A. Prevention B. Internal failure C. External failure D. none of the above 34. One element of a quality system is product verification. This activity includes: A. use of test and inspection points in processes to verify conformance. B. verification of incoming materials. C. final product verification. D. all of the above. E. A and C only.

35.

Ø

.005

A

B

C

In the control frame, datum C is the: A. primary datum. B. tertiary datum. C. basic datum. D. largest datum. 36. Requirements for document control include all of the following except: A. a process for generation, approval, and distribution of documents. B. a process to ensure ready availability of documents where they are needed. C. a process for distribution of revisions and the removal of obsolete documents. D. a process for making the documents available to the general public.



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37. What is the reliability of a system at 850 hours, if the average usage of the system was 400 hours for 1650 items and the total number of failures was 145? Assume an exponential distribution. A. 0% B. 36% C. 18% D. 83% 38. Which material listed below can be usefully tested by the magnetic particle method? A. Carbon steel B. Aluminum C. Magnesium D. Lead E. none of the above 39. If the mean time between failure is found to be 129 hours for a component, what is the reliability of the component at t = 129 hours? A. .720 B. .306 C. .368 D. .785 40. The MTBF for a system is 2500 hours. What is the probability of failure for that system after 2000 hours of operation? A. .37 B. .45 C. .55 D. .82 41. A population of repairable components (having an exponential distribution of life) has an MTBF of 100 hours. What fraction of the components would fail if the population is operated 100 hours? (Failed components are not replaced.) A. 50% B. 37% C. 63% D. 83%




42. Component A has an exponential failure rate of 3 × 10 –4 failures per hour. The life of component B is normally distributed with a mean of 600 and standard deviation of 200 hours. Assuming independence, calculate the reliability of the system after 200 hours. A

B

A. 0.88 B. 0.92 C. 0.95 D. 0.98 43. The difference between an internal and an external audit is: A. an internal audit is conducted indoors while an external audit is conducted outside. B. for an internal audit, the auditors are employees of the auditee organization, whereas for an external audit they are not. C. an external audit is always conducted by a third party. D. an internal audit is done by the auditee in preparation for an external audit. 44. The ASQ definition of quality is: A. the efficient production of products that the customer expects. B. conformance to expectations. C. conformance to specifications. D. the composite of marketing, engineering, and manufacturing, through which the product or service will meet the expectations of the customer. E. the totality of features and characteristics of a product that affect its ability to satisfy a given need. 45. The ASQ Code of Ethics has four sections. They are: A. Policies, Goals, Objectives, Tools. B. Introduction, Definitions, Tools, Summary. C. Fundamental Principles, Relations with the Public, Relations with Employers and Clients, Relations with Peers. D. Leadership, Implementation, Planning, Improvement.



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46. One difference between a “finding” and an “observation” in an audit report is: A. an observation is supported by one or more findings. B. a finding is supported by one or more observations. C. findings detail weaknesses found in the documentation and observations refer to weaknesses in the implementation of the documentation. D. findings are discovered accidentally while observations are intentionally sought. 47. A team studies a coil steel banding process and makes five changes resulting in productivity improvements of 2%, 2.8%, 2.4%, 2%, and 3% respectively. These improvements are best described by which approach to problem solving? A. 5S B. Poka-yoke C. Kaizen D. PDCA E. Reengineering 48. The operators of a manufacturing cell work out a more orderly arrangement for tool storage and establish a schedule to maintain cleanliness on a daily basis. These improvements are best described by which approach to problem solving? A. 5S B. Poka-yoke C. Kaizen D. PDCA E. Reengineering 49. A quality engineer employed by a hospital is asked to improve the process of medication storage in locked cabinets near patient doors. One defect that occurs rarely is that the medication caddy is left out when the cabinet is relocked. The engineer installs a gravity-activated arm that will not permit the door to close when the caddy isn’t inside. This improvement is best described by which approach to problem solving? A. 5S B. Poka-yoke C. Kaizen D. PDCA E. Reengineering




50. A team is investigating ways to reduce power outages. They determine that an outage can occur in only three ways: grid failure, local transformer failure, or local overload. They then investigate each of these three events for possible causes. They draw a diagram that “fans out,” using the power outage as the handle of the fan. These improvements are best described by which approach to problem solving? A. Affinity diagram B. Interrelationship digraph C. Tree diagram D. Process decision program chart E. Matrix diagram F. Prioritization matrix G. Activity network diagram 51. A team’s goal is to improve information flow in a payroll function. They make 33 Post-It notes, each listing an issue for further investigation. After some discussion, they group them into four categories: mandated record keeping, privacy concerns, insurance concerns, and transfer concerns. This grouping process is best described by which approach to problem solving? A. Affinity diagram B. Interrelationship digraph C. Tree diagram D. Process decision program chart E. Matrix diagram F. Prioritization matrix G. Activity network diagram 52. The team in the above problem draws arrows from Post-It notes that are causes to notes that are the effects of these causes. This step is best described by which approach to problem solving? A. Affinity diagram B. Interrelationship digraph C. Tree diagram D. Process decision program chart E. Matrix diagram F. Prioritization matrix G. Activity network diagram



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53. A team working with a plant relocation is tasked with designing a process for moving 180 pieces of equipment. Incoming orders may need to be filled during the move at either the old site or the new one. Transportation equipment availability is uncertain. Construction schedules at the new site are very weather-dependent. The team designs a chart that attempts to cover these and other contingencies with appropriate measures for each. The tool best fitted for this task is: A. Affinity diagram B. Interrelationship digraph C. Tree diagram D. Process decision program chart E. Matrix diagram F. Prioritization matrix G. Activity network diagram 54. A management team lists nine goals across the top of a rectangle and 15 activity initiatives along the left-hand side of the rectangle. If one of the activities strongly supports one of the goals, a circle is placed in the box where that activity’s row intersects the goal’s column. If the activity’s support is very strong, a “bull’seye” is placed in the box; and if the support is weak, a triangle is used. This best describes which problem-solving tool? A. Affinity diagram Goal #1 Goal #2 Goal #3

B. Interrelationship digraph C. Tree diagram D. Process decision program chart

Activity #1

Activity #2

E. Matrix diagram F. Prioritization matrix

Activity #3

G. Activity network diagram 55. The management team in the above problem assigns each goal a numerical value designating its importance. The “bull’s-eyes,” circles, and triangles are replaced by the values 3, 2, and 1 respectively. Entries are made in each box by multiplying the 3, 2, or 1 by the goal value. The importance of each activity is calculated by adding the entries in its row. This best describes which problem-solving tool? A. Affinity diagram #1 (5)

B. Interrelationship digraph C. Tree diagram D. Process decision program chart

Activity #1

#2 (8)

#3 (2)

3 (15)

Activity #2

Total 45

1 (8)

2 (4)

12

E. Matrix diagram F. Prioritization matrix

Activity #3

2 (10)

3 (24)

34

G. Activity network diagram




56. Estimate the standard deviation of the population from which the following set of sample observations was drawn: 26, 31, 31, 27, 24, 29, 29 A. 6.81 B. 28.14 C. 2.61 D. 2.42 57. The expression f(x) = (m x e–m )/x! is the general term for the: A. Poisson distribution. B. Pascal distribution. C. hypergeometric distribution. D. binomial distribution. 58. A number derived from sample data which describes the data in some useful way is called a: A. constant. B. statistic. C. parameter. D. critical value. 59. Estimate the variance of the population which produced the following sample data: 26, 31, 31, 27, 24, 29, 29 A. 6.81 B. 5.84 C. 2.61 D. 2.42 60. Which problem-solving technique is derived from PERT (program evaluation and review technique) and CPM (critical path method) for project management? A. Affinity diagram B. Interrelationship digraph C. Tree diagram D. Process decision program chart E. Matrix diagram F. Prioritization matrix G. Activity network diagram



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61. An example of a measurement scale with an interval rather than a ratio scale is: A. 1 = blue, 2 = green, 3 = red. B. temperature in degrees centigrade. C. area in square millimeters. D. priority ranking, such as first, second, third. 62. Given six books, how many sets can be arranged in lots of three—but always in a different order? A. 18 B. 54 C. 108 D. 120 63. The probability of observing at least one defective in a random sample of size 10, drawn from a population that has been producing, on the average, 10 percent defective units, is: A. (0.10)10 B. (0.90)10 C. 1 – (0.10)10 D. 1 – (0.9)10 64. An accident occurs when both fault event A and fault event B occur. The equation for joint probability of two fault events under any circumstances is given by: P(A&B) = P(A|B) × P(B) where P(A|B) means the probability of fault A given that fault B has occurred. If the occurrence of fault B does not affect the probability of the occurrence of fault A, the probability of an accident, when P(A) = .1 and P(B) = .05, is given by: A. .00025 B. .10000 C. .05000 D. .00500 65. Find system reliability if each component has reliability .92. A. .78 B. .31

A

B

C. .92 D. .97

C

E. .99




66. What is the upper control limit for a p chart (proportion defective) when the average daily production is 2500 units with an established fraction defective of 0.05? A. 0.054 B. 0.058 C. 0.063 D. 0.066 67. An x– and R chart was prepared for an operation using 20 samples with five – pieces in each sample. x– was found to be 33.6 and R was 6.20. During production, a sample of five was taken and the pieces measured 36, 43, 37, 25, and 38. At the time this sample was taken: A. both the average and range were within control limits. B. neither the average nor range were within control limits. C. only the average was outside control limits. D. only the range was outside control limits. 68. In a normal distribution, what is the area under the curve between +0.7 and +1.3 standard deviation units? A. 0.2903 B. 0.7580 C. 0.2580 D. 0.1452 69. Given process average = 1.64, average range = .05, n = 5, assuming statistical control and a normal population, what proportion of the population will meet specifications of 1.65 ± .05? A. 80% B. 86% C. 97% D. 93% 70. The control chart that is most sensitive to variations in measurements is: A. p chart. B. np chart. C. c chart. D. x– and R chart.



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71. If a process is out of control, the theoretical probability that a single point on the x– chart will fall between plus one sigma and the upper control limit is: A. 0.2240 B. 0.1587 C. Unknown D. 0.3413 72. What minimum sample size is required to construct a confidence interval for the mean of a population with standard deviation .035? Assume a confidence level of 98% and a confidence interval width of .02. A. 8 B. 33 C. 21 D. 67 73. In order to be effective, the quality audit function ideally should be: A. an independent organizational segment in the quality control function. B. an independent organizational segment in the production control function. C. an independent organizational segment in the manufacturing operations function. D. all of the above. 74. A purchaser wants to determine whether or not there is any difference between the means of the convolute paperboard cans supplied by two different vendors, A and B. A random sample of 100 cans is selected from the output of each vendor. The sample from A yielded a mean of 13.59 with a standard deviation of 5.94. The sample from B yielded a mean of 14.43 with a standard deviation of 5.61. Which of the following would be a suitable null hypothesis to test? A. mA = mB B. mA > mB C. mA < mB D. mA ≠ mB 75. If a sample size of 16 has an average of 2.53 and a standard deviation of .04, estimate the 95% confidence interval for the population mean, m (assume a normal distribution). A. (2.525, 2.535) B. (2.52, 2.54) C. (2.44, 2.62) D. (2.51, 2.55)




76. The test used for testing significance in an analysis of variance table is: A. the z-test. B. the t-test. C. the F-test. D. the chi-square test. 77. A 43 experiment means that we are considering: A. three levels of four factors. B. four dependent variables and three independent variables. C. four go/no-go variables and three continuous variables. D. four levels of three factors. 78. Use this contingency table to compute P(A|X) (the conditional probability of event A given that event X has occurred): X Y

A 3 5

B 8 6

A. .273 B. .304 C. .426 D. .602 79. In performing an analysis of variance for a single-factor experiment, a fundamental assumption is made that the treatment: A. means are equal. B. means are unequal. C. variances are equal. D. variances are unequal. 80. Dodge-Romig tables are designed to minimize which parameter? A. AOQL B. AQL C. ATI D. AOQ



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ANSWERS TO SIMULATED EXAM (* denotes problems from the sample CQE Exam published in Quality Progress July 1984) 1. C 2. B* 3. A; P(A or B) = P(A) + P(B) – P(A&B) = .83 + .83 – .83 4. B 5. D 6. A 7. D 8. D* 9. C* 10. B 11. C* 12. D* 13. B 14. B 15. B 16. C 17. B 18. A 19. B 20. C* 21. B 22. B 23. C* 24. D 25. D 26. B 27. E* 28. D* *From the sample CQE Exam published in Quality Progress July 1984.




29. D* 30. C 31. E* 32. C; The downward arrow points to the sampling plan for code letter H. 33. E; Such equipment is not considered a quality cost. 34. D 35. B* 36. D 37. D*; l = 145 ÷ [1650 × 400] ≈ .00022; R(850) ≈ e–.00022 × 850 ≈ .83 38. A* 39. C; R(129) = e–129 × 1/129 = e–1 ≈ .368 [Note that it is always true that R(MTBF) ≈ .368] 40. C; l = 1/2500 = .0004 R(2000) = e–.0004 × 2000 ≈ .45 Prob(success) = .45 Prob(failure) = .55 41. C 42. B; For A: R(200) = e–.0003 × 200 ≈ .94 For B, for a normal distribution with m = 600 and s = 200, 200 has a Z-value of –2. The area to the right of –2 is .9772. For the series system, Rs = .94 × .9772. 43. B 44. E 45. C 46. B 47. C 48. A 49. B 50. C 51. A 52. B 53. D 54. E 55. F

*From the sample CQE Exam published in Quality Progress July 1984.



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56. C 57. A 58. B 59. A; Variance is the square of the standard deviation. 60. G 61. B 62. D; The number of orderings or permutations of 6 objects taken 3 at a time is sometimes symbolized 6P3. The formula is nPx = n!/(n – x)! In this case 6!/(6 – 3)! 63. D*; X = number of defectives observed P(X ≥ 1) = P(X > 0) = 1 – P(X = 0) The binomial formula is: P (X = x) =

n− x n! p x (1 − p ) n − x x ! ! ( )

P ( X = 0) =

0 9 9 10 ! 0.1) ( 0.9 ) = ( 0.9 ) ( (10 !× 0 !)

P ( X > 0 ) = 1 − ( 0.9 ) ≈ 0.61 9

64. D; P(A|B) = P(A) so P(A&B) = P(A) × P(B) = .1 × .05 65. E; In the series branch, R = .92 × .92 = .8464. That leaves a parallel system with branches .8464 and .92. 1 – .8464 = .1536, 1 – .92 = .08, Therefore: Rs = 1 – [.1536 × .08] 66. C; p– = 0.05, n = 2500 UCL = p + 3

p (1 − p ) n

= 0.5 + 3 .05 × .95 / 2500 = .05 + 3 ( .0043 ) 6

67. D; UCL = 33.6 + .58 × 6.2 = 37.2 LCL = 33.6 – .58 × 6.2 = 30 UCLr = 2.11 × 6.2 = 13.1 For the sample given, average is 35.8, range = 18. 68. D; From a standard normal table, the area to right of +0.7 = .2420 area to right of 1.3 = .0968 subtracting: .1452





69. C; m = 1.64 USL = 1.70 LSL = 1.60 s ≈ R/d2 = .05/2.33 ≈ .021 QU = (USL – m)/s = (1.70 – 1.64)/.021 = 2.86 QL = (1.60 – 1.54)/.021 = –1.90 Area to right of 2.86 = .0021. Area to left of 1.90 = .0287. Total area outside spec ≈ .03. 70. D 71. C; Since the process is unstable, the probability is unknown. 72. D; za /2 = z. 02/2 = z. 01 = 2 . 33 2

⎡s z ⎤ The formula n ≥ ⎢ a /2 ⎥ is rounded up ⎣ E ⎦ E = margin of error (half of interval width = 0 . 01) ⎡ 0 . 035 ( 2 . 33 ) ⎤ n≥⎢ ⎥ = 66 . 5, n ≥ 67 0 . 01 ⎢⎣ ⎥⎦ 2

73. A 74. A 75. D; x±

ts n

2.53 ±

; t0.025 ,15 = 2.131

2.131 × .04 ≈ 2.53 ± .02 4

76. C 77. D 78. A; Complete the table by totaling rows and columns: X Y

A 3 5 8

B 8 6 14

11 11 22

P(A|X) means we focus on row X which has 11 items, of which three are type A. P(A|X) = 3/11



23

79. C 80. C*




Sample Exam Questions

THIS SECTION WILL NOT BE PERMITTED IN THE CLASSROOM WHILE SITTING FOR THE CERTIFICATION EXAMINATION. Choose the best answer. 1. The ASQ Code of Ethics includes the following four sections: A. Fundamental Principles, Relations with the Public, Relations with Employers and Clients, Relations with Peers. B. Fundamental Principles, Legal Obligations, Relations with Employers and Clients, Relations with Peers. C. Fundamental Principles, Relations with the Public, Relations with Employers and Clients, Rights and Responsibilities. D. Appropriate Use of Knowledge, Relations with the Public, Relations with Employers and Clients, Relations with Peers. E. none of the above. 2. The process of studying “best practices” of other organizations is called: A. good manufacturing practice (GMP). B. quality initiative analysis. C. policy study and deployment (PS&D). D. benchmarking. E. none of the above.

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3. The people responsible for leading quality initiatives need to: A. involve all those impacted in the decision-making process. B. consider the unanticipated consequences of any proposed changes. C. establish a mechanism to verify that the proposed change has the desired affect. D. communicate the results to others. E. all of the above. 4. When strong disagreements arise between team members: A. they should be encouraged to ignore their differences for the sake of team harmony. B. the team should explore data collection schemes that would help determine the correct position. C. the team should vote as soon as possible. D. the team should seek outside mediation to solve the problem. E. the team should report that it is unable to reach a conclusion. 5. The most useful measure of the effectiveness of training is: A. the score on a final examination. B. the opinion survey at the end of the class. C. a pre-test and post-test comparison. D. the impact on processes and products. 6. The expenditure for new equipment to be used for measuring functionality of products belongs in which quality cost category? A. Appraisal B. Prevention C. Internal failure D. External failure E. none of the above


SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Sample Exam Questions

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7. Expenses incurred for a quality engineer’s visit to determine whether a supplier is meeting the specifications on an existing purchase order belong under which quality cost category? A. Appraisal B. Prevention C. Internal failure D. External failure E. none of the above 8. The quality leader most often associated with robust design is: A. Juran B. Deming C. Taguchi D. Ishikawa 9. The quality improvement cycle referred to as either PDCA or PDSA is credited to which two people? A. Deming and Shewhart B. Juran and Ishikawa C. Crosby and Shewhart D. Deming and Taguchi E. Juran Juran 10. Robustness of processes and products refers to: A. the resistance to change caused by environmental variables. B. the ability to produce the first part within specifications. C. the tendency to use less than half the total tolerance. D. the likelihood that the product or service will exceed customer expectations. 11. Customer complaints for products with low unit price are usually: A. easily satisfied. B. not serious. C. underreported. D. due to subterfuge. E. due to shortages.




12. When gathering data about product performance, one should keep in mind that customer complaints: A. provide reliable performance data. B. should be the only source for product performance data. C. are a poor measure of product performance. D. are useless as an indicator of customer satisfaction. 13. Deming recommends single suppliers for purchased parts. Advantages of this approach include: I. size of contract will be larger and therefore may command more attention from supplier. II. minimal disruption due to strikes. III. simplified communication. IV. increased competition between suppliers for each order. A. I and II B. I and IV C. III and IV D. I and III E. II and IV 14. Teams sometimes progress through the development stages referred to as “forming–storming–norming–performing.” In the “norming” stage, the team members: A. each act as normal individuals rather than as a group. B. have opinions that tend to be normally distributed. C. are reluctant to express views that are different from the group consensus. D. begin to shift their focus from personal to team goals. 15. A team facilitator’s main function is to: A. keep the team on task. B. make sure restroom facilities are available. C. provide new ideas for the team to consider. D. select team members.



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16. Joseph Juran delineated three managerial processes that are interrelated as the Juran Trilogy. These may be summarized as: I. quality improvement. II. quality policy. III. quality training. IV. quality mission and vision statement. V. quality planning. VI. quality control. A. IV, V, III B. V, VI, I C. IV, II, VI D. II, IV, V E. II, V, I 17. Ishikawa diagrams are: A. based on the Japanese character for quality. B. cause-and-effect diagrams. C. similar to Pareto charts. D. an alternative to CPM for project management. E. an alternative to flowcharts. 18. Which of the following quality cost indices is likely to have the greatest appeal to top management as an indicator of relative costs? A. Quality cost per unit of product B. Quality cost per hour of direct production labor C. Quality cost per unit of processing cost D. Quality cost per unit of sales E. Quality cost per dollar of direct production labor 19. In spite of the quality engineer’s best efforts, situations may develop in which his or her decision is overruled. The most appropriate action would be to: A. resign the position based on convictions. B. report findings to an outside source, such as a regulatory agency or the press. C. document findings, report to superiors, and move on to the next assignment. D. discuss findings with coworkers in order to gain support, thereby forcing action.




20. Review of purchase orders for quality requirements falls into which one of the following quality cost segments? A. Prevention B. Appraisal C. Internal failures D. External failures 21. Failure costs include costs due to: A. quality control engineering. B. inspection setup for tests. C. certification of special-process suppliers. D. supplier analysis of nonconforming hardware. 22. For a typical month, 900D Manufacturing Company identified and reported the following quality costs: Inspection wages Quality planning Source inspection In-plant scrap and rework Final product test Retest and troubleshooting Field warranty cost

$ 12,000 4,000 2,000 88,000 110,000 39,000 205,000

What is the total failure cost for this month? A. $244,000 B. $151,000 C. $261,000 D. $205,000 E. $332,000 23. A quality system must include two main items—the preparation of documented quality system procedures and instructions, and: A. the effective implementation of the documented quality system procedures and instructions. B. management responsibility. C. purchasing department involvement. D. assessment of subcontractors.



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24. Product verification is an important element of a quality system. Product verification: A. is the procedure used to verify shipping routes and rates. B. is primarily a design function and is not used once the design has been finalized. C. consists of procedures that inform top management that numerical quotas are being met. D. uses tests and inspection in the manufacturing process to verify conformance. E. is a tool used mostly by third-party auditors. 25. Unless otherwise designated, changes to quality documents should be reviewed and approved by: A. top management. B. the same organization/function that produced the original document. C. a team with representation from the quality function. D. the chief quality officer. E. all persons impacted by the changes. 26. The “layers” of a quality manual are: A. policies, procedures, instructions, and records. B. executive, middle management, team leader, and team members. C. financial, product integrity, supplier relations, and process control. D. vision, goals, objectives, and projects. 27. ISO 9000 is an example of: A. a domestic standard. B. an industry association standard. C. an international standard. D. an isometric standard. E. none of the above. 28. You contract with a company to audit one of their offshore suppliers. This type of audit is called a: A. client audit. B. third-party audit. C. follow-up audit. D. registration audit. E. certification audit.




29. The step of the audit that is most often poorly completed is: A. audit team training. B. corrective action and verification. C. notification and preparation. D. execution. E. exit meeting. 30. In most audit situations, it is best for the auditor to: A. notify the auditee in advance, unless prohibited by regulations. B. conduct a surprise audit. C. provide the auditee with the audit checklist at, but not before, the opening meeting. D. none of the above. 31. In an audit team meeting following the exit meeting, a team member suggests an additional finding. It is appropriate to: A. add the finding to the final report. B. omit the finding since it was not mentioned in the exit session. 32. The main purpose of a quality audit is to: A. evaluate management commitment. B. determine whether products conform to specifications. C. assure that the organization is financially sound. D. obtain information regarding the quality system. E. determine whether quality personnel are certified by a third party. 33. ANSI/ISO/ASQ Q9001-2000 replaces: A. ANSI/ISO/ASQC Q9001-1994. B. ANSI/ISO/ASQC Q9002-1994. C. ANSI/ISO/ASQC Q9003-1994. D. all of the above. E. none of the above.



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34. In most systems of classification of quality characteristics: A. there are five categories. B. the highest categories make reference to safety and health. C. the lowest category refers to functional failure. D. fit and finish are not considered in any of the categories. E. none of the above. 35. Strategic plans: A. should be determined after tactical plans are established. B. tend to be more devious than operational plans. C. are longer term than tactical plans. D. are less controversial than tactical plans. E. are used to implement goals established by the tactical plans. 36. A material traceability system provides information on: A. the flow of material through manufacturing processes. B. the raw material used for a particular manufactured item. C. the purchase order to invoice payment process. D. location of items material to pending legal action. E. none of the above. 37. The principle purpose of a material review board (MRB) is to: A. determine the vendor of purchased material. B. make decisions regarding nonconforming material. C. review traceability records for outgoing materials. D. establish policies and procedures for inspecting incoming material. 38. Product traceability is usually used to: A. identify sources of the material used for the product. B. maintain a paper trail of financial transactions regarding the product. C. track the design changes affecting the product. D. maintain payroll records, clock times, and so on, regarding the product.




39. A lot of size 2000 is to be inspected using ANSI/ASQ Z1.4-2003, with an AQL of .65%. Use a single, normal level II plan. The sample size is: A. 20 B. 40 C. 60 D. 80 E. none of the above 40. An attribute sampling plan lists the accept and reject values as Ac = 3, Re = 6. Using the plan correctly, the inspector finds five defectives. The appropriate action is to: A. reject the lot since the number of defectives exceeds the Ac value. B. accept the lot since the number of defectives is less than the Re value. C. draw another sample. D. use a different sampling plan. E. none of the above. 41. A lot is inspected using ANSI/ASQ Z1.4-2003 with an AQL of .65%. The lot passes the inspection. Does that guarantee that the lot has .65% or fewer defectives? A. Yes, because the sampling plans are statistically valid B. Yes, assuming the sample was randomly selected C. No, because Z1.4 isn’t appropriate for use with defectives D. No, because type I error could have occurred E. No, because type II error could have occurred 42. The diagram shows operating characteristic (OC) curves for two sampling plans. The dashed curve is: A. better. B. worse.

Pa

C. better for the consumer. D. better for the producer.

Lot percent defective

43. If the lot is much worse than the AQL, then the sampling plan most likely to detect this at the lowest cost is: A. single. B. double.



11

44. If the same characteristic is measured 10 times, the precision of the measurement system refers to the: A. amount of variation between the 10 readings. B. proximity of the average of the 10 readings to the true value of the characteristic. C. smallest unit measurable. D. general quality of the equipment used. 45. If the same characteristic is measured 10 times, the accuracy of the measurement system refers to the: A. amount of variation between the 10 readings. B. proximity of the average of the 10 readings to the true value of the characteristic. C. smallest unit measurable. D. general quality of the equipment used. 46. The official length of a meter: A. is defined by a brass bar maintained at a constant temperature in Paris. B. varies from country to country due to the lack of an international standard. C. is defined using krypton-86. D. is the distance from the king’s nose to the tip of his middle finger. E. none of the above 47. Accuracy refers to the: A. degree of agreement of measurements with an accepted reference value. B. number of digits on an LCD readout. C. ability to obtain the same value with more than one measuring device. D. ability to get the same answer more than once using the same measurement system. 48. FMEA is most beneficial when used: A. to analyze warranty data. B. to analyze data from the manufacturing process. C. during the design phase. D. in documentation analysis.




49. A technique for translating customer demands into product characteristics is: A. QFD. B. SPC. C. FMEA. D. AQP. E. DOE. 50. A subsurface discontinuity in some purchased steel bar stock is a suspected cause for the high failure rate in your parts fabrication area. All of the following nondestructive test (NDT) methods could be used to screen the bar stock except: A. magnetic particle testing. B. radiographic testing. C. liquid penetrant testing. D. eddy current testing. E. ultrasonic testing. 51. A lot has .15% defective parts. The attribute sampling plan is based on an AQL of .10%. In this situation, the probability of rejection is .936. Find b. A. 0.936 B. 93.6% C. 0.468 D. 0.064 E. none of the above 52. A lot of size 2000 is to be inspected using ANSI/ASQ Z1.4-2003, with an AQL of .15%. Use a single, normal level II plan. The sample size is: A. 20 B. 40 C. 60 D. 80 E. none of the above



13

53. In a reliability test, 135 nonrepairable items are tested for two hours. Eleven failures are observed at the end of the test. The best expression for MTTF is: A. 11/270 B. 270/11 C. 1/135 D. 11 E. 1/11 54. If the time to failure distribution is exponential, the formula for the reliability function is R(t) = e–lt. If MTBF = 285, find R(100). A. R ≈ .704 B. R ≈ .058 C. R ≈ –28,500 D. R ≈ 77,471 55. Refer to the diagram of the bathtub curve. Failures in the area labeled A are usually caused by: A. product design failure. B. fatigue. C. warranty cost. D. manufacturing and/or quality control errors. E. random failure.

A

56. The bathtub curve can be divided into three regions. Which region is most impacted by changes in manufacturing process control? A. Early life region B. Constant failure rate region C. Wear-out region D. Within ±2s of the mean E. none of the above 57. Appropriate labels for the horizontal and vertical axes of a graph of the bathtub curve are, respectively: A. MTBF and time. B. time and failure rate. C. failure rate and MTTF. D. time and number of failures. E. failure rate and time.




58. On the flat part (floor) of the bathtub curve: A. the failure rate is zero. B. the failure rate is increasing. C. the MTBF is constant. D. none of the above. 59. A system has five components in a series, each with a reliability of .999. The system reliability is closest to: A. .995 B. 4.995 C. 1.01 D. .1998 E. 1.000 60. A dual brake system on an automobile allows it to stop safely even if the line to one of the wheels leaks. This is an example of a: A. parallel system. B. series system. C. delayed failure system. D. constant failure rate. E. early failure phase. 61. Knowing the point where the floor of the bathtub curve begins to turn upward helps determine: A. burn-in time. B. failure rate. C. replacement schedule. D. MTTR. E. none of the above. 62. A system has l = .0032 failures per hour and mean time to repair (MTTR) = 11 hours. The steady state availability of the system is approximately: A. .97 B. .0003 C. .035 D. .9997 E. none of the above



15

63. Derating as a design tool is the: A. reduction of the rated reliability of a product or component based on new data. B. assignment of a product to operate at stress levels below its normal rating. C. shortening of the warranty period in order to lower consumer expectations. D. removal of posted ratings from electronic components. 64. FMECA includes tools for: A. determining root causes of failure. B. assessing the probability that a product will function for a stated period of time. C. approximating the capability of a process to hold a certain dimension within tolerance. D. examining a proposed design for possible ways it can fail. 65. In calculating the RPN in an FMEA, the following three values are multiplied together: A. severity, opportunity, difficulty. B. severity, occurrence, design. C. severity, occurrence, detection. D. sensitivity, opportunity, difficulty. 66. Each failure mode and effects analysis (FMEA) report should contain: A. a department to be assigned. B. recommended corrective actions. C. deadline dates. D. return on investment. E. associated project management procedures. 67. A quality team is given a process problem. The team responds by carefully selecting a solution from among several that are proposed. The team next installs the solution and collects the resulting data. Based on this data, the team modifies the solution slightly, then installs it as a permanent part of the process. The best description of the procedure the team has employed is: A. kaizen B. CI C. PDSA D. C.A.R. E. FMEA




68. The Deming (or Shewhart) cycle is a guide for: A. the steps followed in continuous quality improvement. B. milestones in project or process management. C. the procedure used for setting up control charts. D. the design of quality products. E. quality team facilitation. 69. Kaizen activity usually: A. only involves personnel from accounting functions. B. results in a single change that has a large impact on improvement. C. focuses on warranty and customer satisfaction analysis. D. tends to produce many small incremental improvements. 70. An improvement technique known as the Shewhart cycle has the initials PDCA or PDSA. Which element of the cycle is being executed when a team tries out a proposed procedure for improving data integrity? A. P B. D C. C (or S) D. A 71. An improvement technique known as the Shewhart cycle has the initials PDCA or PDSA. Which element of the cycle is being executed when a team decides to make a change in standard operating procedure? A. P B. D C. C (or S) D. A 72. An affinity diagram provides a technique to: A. determine the extent to which two components will operate synergistically. B. evaluate the way different failure modes cascade into one another in a domino effect. C. determine the area under the normal curve as the number of standard deviations increases without bound. D. organize facts and opinions into natural groupings.



17

73. A team wants to reduce the failure rate of a complex system. A useful tool for analyzing the problem would be a: A. histogram. B. scatter diagram. C. Gantt chart. D. tree diagram. 74. A scatter diagram is most often used to provide a visual clue to: A. the existence of correlation. B. changes over time. C. percent defects. D. capability. E. the dispersion of the defects around the product surface. 75. Statistical software calculates that the correlation between mold temperature and shrinkage is .92. This means that: A. the best way to control shrinkage is to control mold temperature. B. changes in mold temperature influence the quality of the product. C. the software is calculating incorrectly. D. mold temperature changes cause shrinkage changes. E. none of the above. 76. A team needs to decide at which points in a process to measure quality characteristics. The quality tool they would use is a: A. control chart. B. check sheet. C. scatter diagram. D. flowchart. E. Pareto chart. 77. A team wants to illustrate which defect types are occurring most frequently. The quality tool they would use is a: A. control chart. B. check sheet. C. scatter diagram. D. flowchart. E. Pareto chart.




78. A team wants to determine whether it might be useful to calculate the correlation coefficient between two variables. The quality tool they would use is a: A. control chart. B. check sheet. C. scatter diagram. D. flowchart. E. capability analysis. 79. A team wants to determine the “shape” of the data to help determine whether it is bimodal as suspected. The quality tool they would use is a: A. control chart. B. histogram. C. scatter diagram. D. flowchart. E. Pareto chart. 80. The first step in determining whether there is a linear association or correlation between two variables is to: A. calculate the correlation coefficient. B. construct a Pareto analysis. C. construct a scatter diagram. D. calculate the standard deviation. E. construct a control chart. 81. When installing corrective action for a quality problem, it is important that: A. documents be updated. B. all involved personnel be informed. C. the process be monitored for recurrence of the problem. D. all of the above. E. none of the above. 82. When completing a corrective action cycle, the most frequently underemphasized step is: A. recurrence control. B. effectiveness assessment. C. problem identification. D. correction.



19

83. The steps in a corrective action cycle, in alphabetical order, are: 1. Correction, 2. Effectiveness assessment, 3. Problem identification, 4. Recurrence control. List these in the order a team should execute them. A. 3, 1, 2, 4 B. 3, 4, 1, 2 C. 3, 4, 2, 1 D. 3, 2, 4, 1 84. In most lists of problem-solving steps, the first step is to: A. install a quick fix. B. analyze the data. C. identify the problem. D. propose several alternative solutions. 85. Many organizations “successfully” solve problems only to have the same problems reoccur at a later date. This is due to failure of: A. recurrence control. B. problem identification. C. cause-and-effect analysis. D. flowchart application. E. matrix diagram implementation. 86. Robust design refers to the ability of a product to: A. fulfill its function for extended periods of time without failure. B. fulfill its function despite changes in operating environment. C. continue operating even though it has suffered damage. D. operate well for various functions in addition to the one for which it was designed. E. none of the above. 87. “Poka-yoke,” or error-proofing, and “robust design” have the following in common: A. both require statistical analysis of data B. both are preventive in nature C. both deal exclusively with products rather than processes D. neither are used in nonmanufacturing processes




88. Most barriers to quality improvement can be attributed to: A. poor attitudes of hourly employees. B. unreasonable requirements by suppliers. C. inadequate statistical knowledge by quality professionals. D. insufficient commitment by management. E. lack of computer power. 89. According to the central limit theorem: A. the median and the mean have the same value in a symmetric distribution. B. the mode of a normal distribution is also the mean. C. the mean of an exponential distribution is smaller than the median. D. the mean, median, and mode of a normal distribution all have the same value. E. none of the above. 90. The term “expected value” is closest to the term: A. median. B. probabilistic model. C. mean. D. Markov value. E. regressive value. 91. A random sample is selected from a population of measurements. The mean of the sample is not equal to the mean of the population. This is due to: A. type I error. B. type II error. C. sampling error. D. abnormal population. E. inexact measurements. 92. A population of size 1,000,000 has mean 42 and standard deviation 6. Sixty random samples of size 15 are selected. According to the central limit theorem, the distribution of the 60 sample means has a mean of approximately: A. 42 B. 42/6 C. 42/15 D. 42 / 15 E. none of the above



21

93. A population of size 1,000,000 has mean 42 and standard deviation 6. Sixty random samples of size 15 are selected. According to the central limit theorem, the distribution of the 60 sample means has a standard deviation of approximately: A. 6 B. 6/42 C. 6/15 D. 6 / 15 E. none of the above 94. A _____ from a sample is used to estimate a population _____. The two words that best fill these blanks are: A. item, value. B. value, statistic. C. statistic, parameter. D. parameter, value. E. parameter, statistic. 95. Deming called the technique of studying a sample to gain understanding of the distribution of a population an “enumerative study.” His main objection to these studies was that: A. they are too difficult to perform correctly. B. they require extensive use of computers. C. they assume a stable distribution. D. random samples are expensive to obtain. E. these studies have a high probability of type II error. 96. If the probability that an event will occur is .83, then the probability that the event will not occur is: A. .17 B. .07 C. .6889 D. ≈ 1.20 E. 83%




97. If the probability that event A occurs is .51, the probability that event B occurs is .64, and the probability that both A and B occur is .23, then: A. events A and B are complementary. B. events A and B are mutually exclusive. C. events A and B are supplementary. D. events A and B are not mutually exclusive. E. events A and B are statistically independent. 98. If the probability that event A occurs is .51, the probability that event B occurs is .64, and events A and B are statistically independent, then: A. A and B are mutually exclusive. B. the probability that both A and B occur is .3264. C. A and B can’t both occur. D. the probability that A occurs is 1 – (probability that B occurs). E. A and B have different standard deviations. 99. The stem-and-leaf plot has the following advantage over the histogram: A. it provides additional information B. it provides a time reference C. it provides a better indication of the shape of the data D. it is more appropriate for discrete data E. none of the above 100. A dimension has tolerance 15.100 to 15.110. To simplify the data collection sheets, the 15 is omitted so values such as .102 are recorded. This is an example of: A. data coding. B. data manipulation. C. stem-and-leaf separation. D. data shifting. E. rational subgroups. 101. Maintaining a good calibration schedule will help with: A. data accuracy. B. data precision. C. data integrity. D. data security. E. data coding.



23

102. A population is bimodal. One hundred samples of size 30 are randomly collected and the 100 sample means are calculated. The distribution of these sample means is: A. bimodal. B. approximately exponential. C. approximately Poisson. D. approximately normal. E. approximately uniform. 103. A population is bimodal with a variance of 5.77. One hundred samples of size 30 are randomly collected and the 100 sample means are calculated. The standard deviation of these sample means is approximately: A. 5.77 B. 2.40 C. 1.05 D. 0.44 E. 0.19 104. An automatic gauging system is to be installed in a process. The gage will insert data values into a database from which machine adjustments will be made automatically. A critical factor in specifying the equipment is: A. the communication link between the gage and the computer. B. the compatibility of software in the the gage and in the computer. C. adequate manual overrides. D. all of the above. 105. The median is a better choice than the mean for a measure of central tendency if the data: A. are bimodal. B. often have outliers. C. are normally distributed. D. are exponentially distributed. 106. Calculate the estimated variance of the population from which the following values have been randomly selected: 2.8 2.7 2.6 2.9 2.8 2.8 2.8. A. .095 B. .009 C. .088 D. .008




107. An advantage of using standard deviation rather than range for measuring dispersion of a large sample is that: A. standard deviation has a simpler formula. B. calculators have a standard deviation key but not a range key. C. standard deviation uses information from each measurement. D. range calculations are not normally distributed. 108. Data are collected in xy pairs and a scatter diagram shows that the points are grouped very close to a straight line that tips down on its right-hand end. A reasonable value for the coefficient of correlation is: A. .8 B. 0 C. –.9 D. 1 E. 1.3 F. –1.8 109. The mean, median, and mode of a distribution have the same value. What can be said about the distribution? A. it is exponential B. it is normal C. it is uniform D. none of the above 110. Approximately what percent of the data values are smaller than the median? A. 25% B. 50% C. 75% D. Between 0% and 99+% inclusive 111. A normal probability plot is used to: A. determine whether the distribution is normal. B. plot z-values. C. determine process capability. D. find percent out of specification.



25

112. The mean of a Poisson distribution is 2.94. Its standard deviation is: A. Not enough information is given B. 1.71 C. 8.64 D. 74.7 E. 1.31 113. The c 2 distribution is: A. symmetric. B. left skewed. C. right skewed. D. normal. E. uniform. 114. The distribution on which p charts are based is called: A. chi-square. B. Poisson. C. bimodal. D. binomial. E. exponential. 115. Ten parts are randomly selected from a normally distributed population. The lengths of the parts are measured and the mean of the 10 values is 10.622. The sample standard deviation of the 10 values is .005. We are 95% confident that the mean of the population from which the sample is drawn is between: A. 10.600 and 10.644 B. 10.615 and 10.629 C. 10.618 and 10.626 D. 10.621 and 10.623 116. An example of a measurement with an interval rather than a ratio scale is: A. 1 = blue, 2 = green, 3 = red. B. temperature in degrees centigrade. C. area in square millimeters. D. length in inches. E. priority ranking, such as first, second, third.




117. Samples of 100 units of blood are tested for the presence or absence of the hepatitis C antibody. The resulting data (number of units per sample testing positive) will form which type of distribution? A. Normal B. Poisson C. Hypergeometric D. Binomial E. Exponential 118. A capability study is done on the diameter of stainless steel rods turned on an automatic lathe. The most appropriate probability distribution to use is: A. normal. B. Poisson. C. hypergeometric. D. binomial. E. exponential. 119. The number of errors per invoice for an accounts receivable function are of interest. The most appropriate probability distribution is: A. normal. B. Poisson. C. hypergeometric. D. binomial. E. exponential. 120. A sample of size 35 is selected from a population of 10,000. The resulting analysis shows that “The 95% confidence interval for the mean is (34.5, 45.6).” This indicates that: A. 95% of the sample values are between 34.5 and 45.6. B. 95% of the population is between 34.5 and 45.6. C. with 95% confidence, it is believed the population mean is between 34.5 and 45.6. D. there is a 95% probability that the sample mean is between 34.5 and 45.6. E. 95% of the sample means are between 34.5 and 45.6.



27

121. A lot of 10,000 pieces of plastic tubing has been received. The purchase order states that the average inside diameter is to be .188 inch. Inside diameter is normally distributed. Twelve pieces are randomly selected. Their inside diameters are: .182 .188 .186 .186 .187 .183 .182 .183 .185 .184 .183 .186 A hypothesis test is performed with null hypothesis H0: m = .188 and alternative hypothesis Ha: m < .188. The critical (or table) value would be (Use a = 0.10): A. 2.681 B. 2.718 C. 3.055 D. 3.106 E. 2.33 122. Paired data collected from a process are: (2.3, 9.7), (2.4, 10.6), (3.5, 12.8), (4.1, 14.2). Use this data to calculate the coefficient of linear correlation. Its value is approximately: A. .58 B. .77 C. –.42 D. 1.29 E. none of the above 123. Paired data collected from a process are: (2.3, 9.7), (2.4, 10.6), (3.5, 12.8), (4.1, 14.2). Calculate the slope of the best-fitting “least squares” linear regression equation. Its value is approximately: A. .58 B. .77 C. –.42 D. 1.29 E. none of the above 124. Assume that a regression equation has slope b1 = 2.5 and intercept b0 = –77.3. Estimate the y-value that would result if x is 12: A. –47.3 B. –925.1 C. –79.8 D. –62.8 E. none of the above




125. Two variables have a linear coefficient of –.97. One concludes from this information that: A. the two variables are not related. B. changes in one of the variables could not be causing changes in the other. C. there is strong evidence that changes in one of the variables causes changes in the other. D. the two variables are strongly correlated. 126. After conducting a hypothesis test, it is concluded that the null hypothesis can not be rejected at the .05 significance level. This means that: A. we can be 95% certain that the null hypothesis is true. B. there is a 95% probability that the alternative hypothesis is false. C. there is a 95% probability that the alternative hypothesis is true. D. there is a 5% probability that the null hypothesis is true. E. none of the above. 127. A pharmaceutical manufacturer is designing an experiment to test four different capsule ingredients designed to reduce dissolution time. Each ingredient will be tested at 10 milligrams and 40 milligrams. A full or complete factorial design is used with five replications per run. The number of levels, factors, and runs is, respectively: A. 2, 4, 16 B. 4, 5, 8 C. 10, 4, 40 D. 2, 20, 16 128. (Refer to the previous problem.) The variance of the five replications for each run is calculated. Most of these variances are approximately equal but two are significantly lower than the others. The experimenters would be especially interested in those two runs if they want to optimize: A. dissolution time. B. interactions. C. main effects. D. robustness. E. degrees of freedom.



29

129. (Refer to the previous problem.) To estimate the within-treatment variance, the experimenters would calculate the variances of: A. all 80 readings. B. the five replications for each run. C. the runs for which a factor is at its lowest level. 130. An experiment has seven factors at two levels each. The experiment has eight runs. This experimental design is called: A. full-factorial design. B. half-fractional factorial design. C. interaction. D. none of the above. 131. The principle disadvantage of fractional factorial experiments is that: A. experimental error is high. B. robustness is compromised. C. effects are confounded. D. measurements are less precise. E. analysis is more difficult. 132. In a resolution III fractional factorial experimental design, main effects are confounded with: A. one-factor interactions. B. two-factor and higher interactions. C. three-factor and higher interactions. D. no other effects. 133. A process needs a constant pH of between 5 and 9 to operate efficiently. A control chart is used to monitor the acidity of a well-stirred vat of liquid. An x– and R chart is used with n = 4. The four readings in each sample are collected from the four compass points approximately 4" from the outside of the round vat. The x– and R chart is not a good choice for this situation because it will probably show: A. very few points outside control limits even when the process is not stable. B. a large number of points outside the control limits even when the process is stable. C. the process is not normally distributed. D. the average run length (ARL) is excessive.




134. A stable, normally distributed process with specification 3.50 ± .03 has m = 3.51 and s = .016. What percent of the production violates specification? A. 16.43% B. 12.62% C. 18.58% D. 11.18% 135. (Refer to the previous problem.) Find Cp and Cpk. A. 1.21 and .85 B. .85 and 1.21 C. .35 and .63 D. .63 and .42 E. None of the above 136. The following data were collected on the diameters of turned shafts: 2.506 2.508 2.505 2.505. These values are: I. attribute data. II. discrete data. III. variables data. IV. continuous data. A. I and II B. I only C. II only D. I and IV E. III and IV



31

ANSWERS TO SAMPLE EXAM QUESTIONS 1. A 2. D 3. E 4. B 5. D 6. E 7. A; A visit to determine whether a contract should be awarded belongs in the prevention category. 8. C 9. A 10. A 11. C 12. C 13. D 14. D 15. A 16. B 17. B 18. D 19. C 20. A 21. D 22. E 23. A 24. D 25. B 26. A 27. C 28. B 29. B 30. A




31. B 32. D 33. D 34. B 35. C 36. B 37. B 38. A 39. E; The correct answer is 125. 40. C 41. E 42. D 43. B 44. A 45. B 46. E; The krypton-based standard was replaced in 1975 with one based on the distance traveled by a light wave in a stated period of time. 47. A 48. C 49. A 50. C 51. D; b is the probability of failing to reject a bad lot. Since the probability of rejection is .936, the probability of not rejecting is 1 – .936. 52. D; Note that the arrow in the table points to the plan with code letter J. 53. B; l = failure rate = (# failures) ÷ (total test time). Then calculate MTTF = 1/l. 54. A; l = 1/MTBF = 1/285 ≈ .003509

R(100) ≈ e (–.0035)(100) = .704

55. D 56. A 57. B 58. C; Since the failure rate is constant, MTBF will be also. 59. A; Rs = .9995 60. A



33

61. C; It is often appropriate to recommend replacement as the wear-out phase begins and l rises. 62. A; Solution note: A = MTBF/(MTBF + MTTR) 63. B 64. D 65. C 66. B 67. C 68. A 69. D 70. B 71. D 72. D 73. D 74. A 75. E 76. D; Although all these could be used, the team would almost invariably use a flowchart also. 77. B 78. C; Before calculating r, it is best to view the data on a scatter diagram to see if there appears to be an association between the variables. 79. B 80. C 81. D 82. A 83. A 84. C 85. A; Cause-and-effect analysis could be faulty, but even if the CE were perfect, a failure could occur if recurrence control is poor. 86. B 87. B 88. D 89. E




90. C 91. C 92. A 93. D; The formula for approximating the standard deviation of the distribution of sample means is s / n . 94. C 95. C 96. A; P(not A) = 1 – P(A) 97. D; If A and B are mutually exclusive, P(A&B) = 0. Since P(A&B) = .23, they are not mutually exclusive, indicating that D is the correct response. Could E be correct also? If the events are statistically independent, then P(A&B) = P(A)P(B). Since this equation is not valid, the events are not statistically independent. 98. B; Since the events are statistically independent, P(A&B) = P(A)P(B). 99. A 100. A 101. A 102. D; This is an application of the central limit theorem. 103. D; The population standard deviation is .577 ≈ 2.40. The formula for standard deviation of the distribution of sample means is s / n ≈ 2.40 / 30 . 104. D 105. B; The median tends to be minimally influenced by an outlier. Consider the set 3, 4, 6, 8, 8, 12, which has a median of 7. If the 6 is misread as 60, the set would be 3, 4, 8, 8, 12, 60. The median would still be 8 although the mean has gone from 6.8 in the original set to 15.8 in the new set. 106. B; Find the estimated standard deviation of the population and square it to obtain the variance. 107. C 108. C 109. D; Any symmetric distribution with mode at the axis of symmetry will have mean = median = mode. 110. B 111. A 112. B; The mean and variance of a Poisson distribution are equal. 113. C



35

114. D 115. C; The confidence interval formulas are: x ± ts / n Substituting: 10.622 ± t.025 ,9 ( .005 ) / 10 ≈ 10.622 ± .004 116. B 117. D 118. A 119. B 120. C 121. A; x– ≈ .185

s ≈ .002 The confidence interval formula is x ± ts / n .

Substituting: .185 ± t0.05 ,11 ( .002 ) / 12 = .185 ± 1.796 ( .002 ) / 12 ≈ .185 ± .001 122. E; x 2.3 2.4 3.5 4.1 Σ mean

r=

12.3 3.075

y

xy

x2

y2

22.31 25.44 44.8 58.2

5.29 5.76 12.25 16.81

94.09 112.36 163.84 201.64

150.75

40.11

571.93

9.7 10.6 12.8 14.2 47.3 11.825

Sxy Sxx Syy

where Sxx = Σx2 – (Σx)2/n = 40.11 – 12.32/4 = 2.2875 Sxy = Σxy – (Σx)(Σy)/n = 150.75 – 12.3 × 47.3/4 = 5.3025 Syy = Σy2 – (Σy)2/n = 571.93 – 47.32/4 = 12.6075 Substituting, r = ( 5.3025 ) /

( 2.2875) (12.6075) ≈ .99

123. E; b1 = Sxy/Sxx = 5.3025/2.2575 ≈ 2.3 (Incidentally, b = y– – b ; x– = 11.825 – 2.3 × 3.075 ≈ 4.75) 0

1

124. A; y = 2.5x – 77.3 = 2.5 × 12 – 77.3 125. D; Correlation, even though high, does not imply causation. 126. E 127. A; The formula for number of runs for a full-factorial experiment: (#levels)(#factors). 128. D; The most robust design occurs when the environmental factors (those not being controlled) have the least impact on the quality characteristic being studied.




129. B; Within-treatment variation is the variation that occurs when the treatments (levels and factors) are not changed. 130. D; A full factorial would have 27 = 128 runs. A half factorial would have 64 runs. This is a one-sixteenth fractional factorial. 131. C 132. B 133. B 134. D; QL = .04/.016 = 2.5 from a normal table, .62% violates the lower specification QU = .02/.016 = 1.25 from a normal table, 10.56% violates the upper specification 135. D; Cp = (USL – LSL) ÷ (6s ) = (3.53 – 3.47) ÷ (.096) ≈ .63 Cpk = Min(QL, QU) ÷ 3 = Min(2.5, 1.25) ÷ 3 = 1.25 ÷ 3 ≈ .42 136. E



Acceptance Sampling Tables

1


*

* * *

* *

0

0

* * *

* * *

0

0

20000–31499

31500 & Over 24

14

7

4

2

0

0

*

* * *

* * *

* * *

38

22

13

7

3

1

0

0

* * *

* * *

* * *

67

40

24

14

7

4

2

0

0

*

* *

186

115

68 111

69

42

25

14

8

4

2

0

0

* *

* * *

181

110

68

40

24

14

7

4

2

0

0

* * *

* * *

40

24

14

8

4

2

0

0

* * *

* * *

* * *

* * *

169

105

63

38

22

13

7

3

1

0

0

*

* * *

181

110

67

40

24

14

7

4

2

0

0

* * *

2.5

181

111

68

49

24

14

8

4

2

0

0

* *

4.0

186

115

69

42

25

14

8

4

2

0

0

*

6.5

181

105

68

181

169

181

63

110

113

68 110

39

189

471 277 181

115

68

40

22

14

40

490 301 177

72

42

25

24

297 181 105

68 115

42

24

14

7 13

301 181

110

63

40

25

14

7

3

2

7

277 178

63

36

22

4

181 115

68 105

40

22

14

8

650 1000

13

400

7

250

4

150

3

100

2

65

1

40

0

25

0

15

0

10

= Denotes that the number of sample units from the last ten lots or batches is not sufficient for reduced inspection for this AQL. Is this instance, more than ten lots or batches may be used for the calculation, provided that the lots or batches used are the most recent ones in sequence, that they have all been on normal inspection, and that none has been rejected while on original inspection.

14

8

8

4

2

4

4

2

0

0

* *

* * *

2

0

0

* * *

* * *

* * *

* * *

0

0

* * *

* * *

* * *

1.5

Figure 1 ANSI/ASQ Z1.4-2003 Table VIII: Limit numbers for reduced inspection.

*

12500–19999

8000–12499

5000–7999

3150–4999

2000–3149

1250–1999

800–1249

1

* * *

* * *

* * *

500–799

320–499

200–319

* * *

* * *

* * *

130–199

80–129

50–79

* * *

* * *

30–49

20–29

1.0

Acceptance Quality Limits

2

* * *

Number of sample units from last 10 lots or batches 0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65

SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Acceptance Sampling Tables


to to to

to to to

151 281 501

1201 3201 10001 3200 10000 35000

280 500 1200

50 90 150

8 15 25

D D D

C C C

B B C

A B B

A A A

S-1

Figure 2 ANSI/ASQ Z1.4-2003 Table I: Sample size code letters.

to 150000 to 500000 and over

to to to

26 51 91

35001 150001 500001

to to to

2 9 16

Lot or batch size

E E E

D D D

C C C

B B B

A A A

S-2

G G H

E F F

D D E

B C C

A A B

S-3

Special inspection levels

J J K

G G H

E E F

C C D

A A B

S-4

L M N

H J K

E F G

C C D

A A B

I

N P Q

K L M

G H J

D E F

A B C

II

P Q R

L M N

H J K

E F G

B C D

III

General inspection levels

SINGLE-USER LICENSE ONLY, COPYING AND NETWORKING PROHIBITED. Acceptance Sampling Tables 3


8 13 20

32 50 80

125 200 315

500 800 1250

2000

D E F

G H J

K L M

N P Q

R

1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 30 31 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 30 31 44 45 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 30 31 44 45

2 3 3 4 5 6 7 8 10 11 14 15 21 22 3 4 5 6 7 8 10 11 14 15 21 22 5 6 7 8 10 11 14 15 21 22

1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 30 31 44 45 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 30 31 44 45 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

0 1

= Use the first sampling plan above the arrow.

= Use the first sampling plan below the arrow. If sample size equals, or exceeds, lot size, carry out 100 percent inspection.

2 3 3 4 5 6 7 8 10 11 14 15 21 22

3 4 5 6 7 8 10 11 14 15 21 22 5 6 7 8 10 11 14 15 21 22 7 8 10 11 14 15 21 22

1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22 2 3 3 4 5 6 7 8 10 11 14 15 21 22

1 2

1 2

0 1

1 2 1 2 2 3 1 2 2 3 3 4

1 2 2 3 1 2 2 3 3 4 1 2 2 3 3 4 5 6

0 1

0 1

0 1

Re = Rejection number.

Ac = Acceptance number.

0 1

0 1

0 1

0 1

0 1

0 1

0 1

0 1

0 1

0 1

Figure 3 ANSI/ASQ Z1.4-2003 Table II-A: Single sampling plans for normal inspection.

2 3 5

4

A B C

Acceptance Quality Limits, AQLs, in Percent Nonconforming Items and Nonconformities per 100 Items (Normal Inspection)

Sample size Sample 0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000 code size letter Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re



Sample size code letter

Sample size

Sample

4.0

6.5

10

15

25

40

65

✸

✸

0 1

✸

0 1 2 0 2 3

✸

2 2 3 4

0 1 0 3 1 4

✸

2 2 3 4 4 5

0 1 0 3 1 4 2 6

✸

2 2 3 4 4 5 5 7

0 1 0 3 1 4 2 6 3 8

✸

2 2 3 4 4 5 5 7 7 9

0 2 1 2 0 3 3 4 1 4 4 5 2 5 6 7 3 7 8 9 5 9 12 13

✸ 0 2 1 2 0 2 0 3 1 2 3 4 0 3 1 4 3 4 4 5 1 4 2 5 4 5 6 7 2 5 3 7 6 7 8 9 3 7 5 9 8 9 12 13 5 9 7 11 12 13 18 19 7 11 11 16 18 19 26 27

✸

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26 2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

✸

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

0 1 0 3 1 4 2 6 3 8 5 12 7 18 11 26

2 2 3 4 4 5 5 7 7 9 9 13 11 19 16 27

0 3 1 4 2 6 3 8 5 12 7 18 11 26

3 4 4 5 5 7 7 9 9 13 11 19 16 27

+ 1 4 2 6 3 8 5 12 7 18 11 26

4 5 5 7 7 9 9 13 11 19 16 27

+ 2 6 3 8 5 12 7 18 11 26

5 7 7 9 9 13 11 19 16 27

+

= Use first sampling plan above arrow.

= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection.

2 4 3 6 5 10 8 16 13 26 20 40 32 64 50 100 80 160 125 250 200 400 315 630 500 1000 800 1600 1250 2500

Cumulative sample size

+ = Use corresponding single sampling plan or double sampling plan for code letter B below.

✸ = Use corresponding single sampling plan.



2 First 2 Second 3 First 3 Second 5 First 5 Second 8 First 8 Second 13 First 13 Second 20 First 20 Second 32 First 32 Second 50 First 50 Second 80 First 80 Second 125 First Second 125 200 First Second 200 315 First Second 315 500 First Second 500 800 First Second 800 1250 First Second 1250

2.5

100

150

250

400

650 1000

3 8 5 12 7 18 11 26

7 9 9 13 11 19 16 27

+ 5 12 7 18 11 26 17 37

9 13 11 19 16 27 22 38

+ 7 18 11 26 17 37 25 56

11 19 16 27 22 38 31 57

+ 16 27 22 38 31 57

+ 11 26 17 37 25 56

+ 22 25 31 38 56 57 31 57

+ 17 37 25 56


Figure 4 ANSI/ASQ Z1.4-2003 Table III-A: Double sampling plans for normal inspection.

R

Q

P

N

M

L

K

J

H

G

F

E

D

C

B

A

1.5

Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re

1.0

Acceptance Quality Limits (reduced inspection)

0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65




Sample

2 4 6 8 10 12 14 3 6 9 12 15 18 21 5 10 15 20 25 30 35 8 16 24 32 40 48 56 13 26 39 52 65 78 91 20 40 60 80 100 120 140

1.5

2.5

4.0

6.5

10

✸

✸

✸

# # 0 0 1 1 2

✸

2 2 2 3 3 3 3

# # 0 0 1 1 2 # 0 0 1 2 3 4

✸

2 2 2 3 3 3 3 2 3 3 4 4 5 5

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6

✸

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7

# 2 # 2 0 2 0 3 1 3 1 3 2 3 # 2 0 3 0 3 1 4 2 4 3 5 4 5 # 3 0 3 1 4 2 5 3 6 4 6 6 7 # 4 1 5 2 6 3 7 5 8 7 9 9 10

✸

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14

✸ # # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19

✸

# 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25

= Use first sampling plan above arrow.

++ = Use corresponding double sampling plan or multiple sampling plan for code letter D below.

2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26

++

= Use first sampling plan below arrow (refer to continuation of table on following page, when necessary). If sample size equals or exceeds lot or batch size, do 100 percent inspection.

2 2 2 2 2 2 2 3 3 3 3 3 3 3 5 5 5 5 5 5 5 8 8 8 8 8 8 8 13 13 13 13 13 13 13 20 20 20 20 20 20 20

Sample size


First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh


Figure 5 ANSI/ASQ Z1.4-2003 Table IV-A: Multiple sampling plans for normal inspection.

J

H

G

F

E

D

A B C

1.0

15

25

40

65

100

150

250

400

650 1000

# 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

++ ++ 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

✸ ++ ++ 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

✸ ++ ++ 1 4 8 12 17 21 25 2 7 13 19 25 31 37

7 10 13 17 20 23 26 9 14 19 25 29 33 38

✸ ++ ++ 2 7 13 19 25 31 37 4 11 19 27 36 45 53

9 14 19 25 29 33 38 12 19 27 34 40 47 54

✸ ++ ++ 4 11 19 27 36 45 53 6 17 29 40 53 65 77

12 19 27 34 40 47 54 16 27 39 49 58 68 78

✸ ++ ++ 6 17 29 40 53 65 77

16 27 39 49 58 68 78

✸ ++ ++

✸ ## ##

✸ ##

## = Use corresponding double sampling plan.

# = Acceptance not permitted at this sample size.



# 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

✸ ++ ++


6

0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65

Acceptance Quality Limits (normal inspection)




32 32 32 32 32 32 32 50 50 50 50 50 50 50 80 80 80 80 80 80 80 125 125 125 125 125 125 125 200 200 200 200 200 200 200 315 315 315 315 315 315 315 500 500 500 500 500 500 500

32 64 96 128 160 192 224 50 100 150 200 250 300 350 80 160 240 320 400 480 560 125 250 375 500 625 750 875 200 400 600 800 1000 1200 1400 315 630 945 1260 1575 1890 2205 500 1000 1500 2000 2500 3000 3500


Sample

✸

✸

# # 0 0 1 1 2

✸

2 2 2 3 3 3 3

# # 0 0 1 1 2 # 0 0 1 2 3 4

✸

2 2 2 3 3 3 3 2 3 3 4 4 5 5

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6

✸

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7

# 2 # 2 0 2 0 3 1 3 1 3 2 3 # 2 0 3 0 3 1 4 2 4 3 5 4 5 # 2 0 3 1 4 2 5 3 6 4 6 6 7 # 4 1 5 2 6 3 7 5 8 7 9 9 10

✸

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18

2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19



= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection.

Sample size

First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh First Second Third Fourth Fifth Sixth Seventh

1.5

2.5

4.0

6.5

10

15

25

40

65

100

150

250

400

650 1000

# # 0 0 1 1 2 # 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25

2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

# 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

# 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

5 8 10 13 15 17 19 7 10 13 17 20 23 26 9 14 19 25 29 33 38

1 4 8 12 17 21 25 2 7 13 19 25 31 37

7 10 13 17 20 23 26 9 14 19 25 29 33 38

2 7 13 19 25 31 37

9 14 19 25 29 33 38

= Use first sampling plan above arrow (refer to preceding page, when necessary).

# 0 0 1 2 3 4 # 0 1 2 3 4 6 # 1 2 3 5 7 9 0 1 3 5 7 10 13 0 3 6 8 11 14 18 1 4 8 12 17 21 25 2 7 13 19 25 31 37

# = Acceptance not permitted at this sample size.


2 2 2 3 3 3 3 2 3 3 4 4 5 5 3 3 4 5 6 6 7 4 5 6 7 8 9 10 4 6 8 10 11 12 14 5 8 10 13 15 17 19 7 10 13 17 20 23 26


Figure 5 Continued

R

Q

P

N

M

L

K

1.0


0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65

Acceptance Quality Limits (normal inspection)





Inspection Levels Lot Size

Special S3 S4

General I II III

2

to

8

B

B

B

B

C

9

to

15

B

B

B

B

D

16

to

25

B

B

B

C

E

26

to

50

B

B

C

D

F

51

to

90

B

B

D

E

G

91

to

150

B

C

E

F

H

151

to

280

B

D

F

G

I

281

to

400

C

E

G

H

J

401

to

500

C

E

G

I

J

501

to

1,200

D

F

H

J

K

1,201

to

3,200

E

G

I

K

L

3,201

to

10,000

F

H

J

L

M

10,001

to

35,000

G

I

K

M

N

35,001

to

150,000

H

J

L

N

P

150,001

to

500,000

H

K

M

P

P

over

H

K

N

P

P

500,001 and

Figure 6 ANSI/ASQ Z1.9-2003 Table A-21: Sample size code letters. 2 1

The theory governing inspection by variables depends on the properties of the normal distribution and, therefore, this method of inspection is only applicable when there is reason to believe that the frequency distribution is normal. 2 Sample size code letters given in body of table are applicable when the indicated inspection levels are to be used.




Sample Size Code Letter

Acceptance Quality Limits (normal inspection) Sample Size

T

.10

.15

.25

.40

.65

1.00

1.50

2.50

4.00

k

k

k

k

k

k

k

k

k

k

6.50 10.00

.587

.502

.401 .296

.651

.598

.525

.450

.364 .276

k

k

B

3

C

4

D

5

.663 .614

.565

.498

.431

.352 .272

E

7

.702 .659

.613 .569

.525

.465

.405

.336 .266

F

10

.916 .863 .811

.755 .703

.650

.579

.507

.424 .341

G

15

1.04

.958 .903 .850

.792 .738

.684

.610

.536

.452 .368

H

25

1.10

1.05

1.01

.951 .896

.835 .779

.723

.647

.571

.484 .398

I

30

1.10

1.06

1.02

.959 .904

.843 .787

.730

.654

.577

.490 .403

J

40

1.13

1.08

1.04

.978 .921

.860 .803

.746

.668

.591

.503 .415

K

60

1.16

1.11

1.06 1.00

.948

.885 .826

.768

.689

.610

.521 .432

L

85

1.17

1.13

1.08 1.02

.962

.899 .839

.780

.701

.621

.530 .441

M

115

1.19

1.14

1.09 1.03

.975

.911 .851

.791

.711

.631

.539 .449

N

175

1.21

1.16

1.11 1.05

.994

.929 .868

.807

.726

.644

.552 .460

P

230

1.21

1.16

1.12 1.06

.996

.931 .870

.809

.728

.646

.553 .462

.10

.15

.40

.65

1.00 1.50

2.50

4.00

6.50 10.00

.999

.25

Acceptance Quality Limits (tightened inspection) All AQL values are in percent nonconforming. T denotes plan used exclusively on tightened inspection and provides symbol for identification of appropriate OC curve. Use first sampling plan below arrow; that is, both sample size as well as k value. When sample size equals or exceeds lot size, every item in the lot must be inspected.

Figure 7 ANSI/ASQ Z1.9-2003 Table C-1: Master table for normal and tightened inspection for plans based on variability unknown (single specification limit—form 1).



QU or QL


Sample Size 3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

.1 .2 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39

50.00 47.24 44.46 41.63 41.35 41.06 40.77 40.49 40.20 39.91 39.62 39.33 39.03

50.00 46.67 43.33 40.00 39.67 39.33 39.00 38.67 38.33 38.00 37.67 37.33 37.00

50.00 46.44 42.90 39.37 39.02 38.67 38.32 37.97 37.62 37.28 36.93 36.58 36.23

50.00 46.26 42.54 38.87 38.50 38.14 37.78 37.42 37.06 36.69 36.33 35.98 35.62

50.00 46.16 42.35 38.60 38.23 37.86 37.49 37.12 36.75 36.38 36.02 35.65 35.29

50.00 46.10 42.24 38.44 38.06 37.69 37.31 36.94 36.57 36.20 35.83 35.46 35.10

50.00 46.08 42.19 38.37 37.99 37.62 37.24 36.87 36.49 36.12 35.75 35.38 35.02

50.00 46.06 42.16 38.33 37.95 37.58 37.20 36.83 36.45 36.08 35.71 35.34 34.97

50.00 46.05 42.15 38.31 37.93 37.55 37.18 36.80 36.43 36.05 35.68 35.31 34.94

50.00 46.05 42.13 38.29 37.91 37.54 37.16 36.78 36.41 36.04 35.66 35.29 34.93

50.00 46.04 42.11 38.27 37.89 37.51 37.13 36.75 36.38 36.01 35.63 35.26 34.89

50.00 46.03 42.10 38.25 37.87 37.49 37.11 36.73 36.36 35.98 35.61 35.24 34.87

50.00 46.03 42.09 38.24 37.86 37.48 37.10 36.72 36.35 35.97 35.60 35.23 34.86

50.00 46.02 42.09 38.23 37.85 37.47 37.09 36.71 36.34 35.96 35.59 35.22 34.85

50.00 46.02 42.08 38.22 37.84 37.46 37.08 36.71 36.33 35.96 35.58 35.21 34.84

.40 .41 .42 .43 .44 .45 .46 .47 .48 .49

38.74 38.45 38.15 37.85 37.56 37.26 36.96 36.66 36.35 36.05

36.67 36.33 36.00 35.67 35.33 35.00 34.67 34.33 34.00 33.67

35.88 35.54 35.19 34.85 34.50 34.16 33.81 33.47 33.12 32.78

35.26 34.90 34.55 34.19 33.84 33.49 33.13 32.78 32.43 32.08

34.93 34.57 34.21 33.85 33.49 33.13 32.78 32.42 32.07 31.72

34.73 34.37 34.00 33.64 33.28 32.92 32.57 32.21 31.85 31.50

34.65 34.28 33.92 33.56 33.20 32.84 32.48 32.12 31.77 31.41

34.60 34.24 33.87 33.51 33.15 32.79 32.43 32.07 31.72 31.36

34.58 34.21 33.85 33.48 33.12 32.76 32.40 32.04 31.69 31.33

34.56 34.19 33.83 33.46 33.10 32.74 32.38 32.02 31.67 31.31

34.53 34.16 33.79 33.43 33.07 32.71 32.35 31.99 31.63 31.28

34.50 34.13 33.77 33.40 33.04 32.68 32.32 31.96 31.61 31.25

34.49 34.12 33.76 33.39 33.03 32.67 32.31 31.95 31.60 31.24

34.48 34.11 33.75 33.38 33.02 32.66 32.30 31.94 31.58 31.23

34.47 34.11 33.74 33.38 33.01 32.65 32.29 31.93 31.58 31.22

.50 .51 .52 .53 .54 .55 .56 .57 .58 .59

35.75 35.44 35.13 34.82 34.51 34.20 33.88 33.57 33.25 32.93

33.33 33.00 32.67 32.33 32.00 31.67 31.33 31.00 30.67 30.33

32.44 32.10 31.76 31.42 31.08 30.74 30.40 30.06 29.73 29.39

31.74 31.39 31.04 30.70 30.36 30.01 29.67 29.33 28.99 28.66

31.37 31.02 30.67 30.32 29.98 29.64 29.29 28.95 28.61 28.28

31.15 30.80 30.45 30.10 29.76 29.41 29.07 28.73 28.39 28.05

31.06 30.71 30.36 30.01 29.67 29.32 28.98 28.64 28.30 27.96

31.01 30.66 30.31 29.96 29.62 29.27 28.93 28.59 28.25 27.92

30.98 30.63 30.28 29.93 29.59 29.24 28.90 28.56 28.22 27.89

30.96 30.61 30.26 29.91 29.57 29.22 28.88 28.54 28.20 27.87

30.93 30.57 30.23 29.88 29.53 29.19 28.85 28.51 28.17 27.83

30.90 30.55 30.20 29.85 29.51 29.16 28.82 28.48 28.14 27.81

30.89 30.54 30.19 29.84 29.49 29.15 28.81 28.47 28.13 27.79

30.88 30.53 30.18 29.83 29.48 29.14 28.80 28.46 28.12 27.78

30.87 30.52 30.17 29.82 29.48 29.13 28.79 28.45 28.11 27.78

.60 .61 .62 .63 .64 .65 .66 .67 .68 .69

32.61 32.28 31.96 31.63 31.30 30.97 30.63 30.30 29.96 29.61

30.00 29.67 29.33 29.00 28.67 28.33 28.00 27.67 27.33 27.00

29.05 28.72 28.39 28.05 27.72 27.39 27.06 26.73 26.40 26.07

28.32 27.98 27.65 27.32 26.99 26.66 26.33 26.00 25.68 25.35

27.94 27.60 27.27 26.94 26.61 26.28 25.96 25.63 25.31 24.99

27.72 27.39 27.05 26.72 26.39 26.07 25.74 25.42 25.10 24.78

27.63 27.30 26.96 26.63 26.31 25.98 25.66 25.33 25.01 24.70

27.58 27.25 26.92 26.59 26.26 25.93 25.61 25.29 24.97 24.65

27.55 27.22 26.89 26.56 26.23 25.90 25.58 25.26 24.94 24.62

27.53 27.20 26.87 26.54 26.21 25.88 25.56 25.24 24.92 24.60

27.50 27.16 26.83 26.50 26.18 25.85 25.53 25.21 24.89 24.57

27.47 27.14 26.81 26.48 26.15 25.83 25.51 25.19 24.87 24.55

27.46 27.13 26.80 26.47 26.14 25.82 25.49 25.17 24.86 24.54

27.45 27.11 26.78 26.46 26.13 25.81 25.48 25.16 24.85 24.53

27.44 27.11 26.78 26.45 26.12 25.80 25.48 25.16 24.84 24.52

0

Figure 8 ANSI/ASQ Z1.9-2003 Table B-5: Table for estimating the lot percent nonconforming using standard deviation method.1 1

Values tabulated are read in percent.



11

QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

.70 .71 .72 .73 .74 .75 .76 .77 .78 .79

29.27 28.92 28.57 28.22 27.86 27.50 27.13 26.76 26.39 26.02

26.67 26.33 26.00 25.67 25.33 25.00 24.67 24.33 24.00 23.67

25.74 25.41 25.09 24.76 24.44 24.11 23.79 23.47 23.15 22.83

25.03 24.71 24.39 24.07 23.75 23.44 23.12 22.81 22.50 22.19

24.67 24.35 24.03 23.72 23.41 23.10 22.79 22.48 22.18 21.87

24.46 24.15 23.83 23.52 23.21 22.90 22.60 22.30 21.99 21.70

24.38 24.06 23.75 23.44 23.13 22.83 22.52 22.22 21.92 21.63

24.33 24.02 23.71 23.40 23.09 22.79 22.48 22.18 21.89 21.59

24.31 23.99 23.68 23.37 23.07 22.76 22.46 22.16 21.86 21.57

24.29 23.98 23.67 23.36 23.05 22.75 22.44 22.14 21.85 21.55

24.26 23.95 23.64 23.33 23.02 22.72 22.42 22.12 21.82 21.53

24.24 23.92 23.61 23.31 23.00 22.70 22.40 22.10 21.80 21.51

24.23 23.91 23.60 23.30 22.99 22.69 22.39 22.09 21.78 21.50

24.22 23.90 23.59 23.29 22.98 22.68 22.38 22.08 21.79 21.49

24.21 23.90 23.59 23.28 22.98 22.68 22.38 22.08 21.78 21.49

.80 .81 .82 .83 .84 .85 .86 .87 .88 .89

25.64 25.25 24.86 24.47 24.07 23.67 23.26 22.84 22.42 21.99

23.33 23.00 22.67 22.33 22.00 21.67 21.33 21.00 20.67 20.33

22.51 22.19 21.87 21.56 21.24 20.93 20.62 20.31 20.00 19.69

21.88 21.58 21.27 29.97 20.67 20.37 20.07 19.78 19.48 19.19

21.57 21.27 20.98 29.68 20.39 20.10 19.81 19.52 19.23 18.95

21.40 21.10 20.81 20.52 20.23 19.94 19.66 19.38 19.10 18.82

21.33 21.04 20.75 20.46 20.17 19.89 19.60 19.32 19.05 18.77

21.29 21.00 20.71 20.42 20.14 19.86 19.57 19.30 19.02 18.74

21.27 20.98 20.69 20.40 20.12 19.84 19.56 19.28 19.00 18.73

21.26 20.97 20.68 20.39 20.11 19.82 19.54 19.27 18.99 18.72

21.23 20.94 20.65 20.37 20.09 19.80 19.53 19.25 18.98 18.70

21.22 20.93 20.64 20.35 20.07 19.79 19.51 19.24 18.96 18.69

21.21 20.92 20.63 20.35 20.06 19.78 19.51 19.23 18.96 18.69

21.20 20.91 20.62 20.34 20.06 19.78 19.50 19.23 18.95 18.68

21.20 20.91 20.62 20.34 20.05 19.77 19.50 19.22 18.95 18.68

.90 .91 .92 .93 .94 .95 .96 .97 .98 .99

21.55 21.11 20.66 20.19 19.73 19.25 18.75 18.25 17.74 17.21

20.00 19.67 19.33 19.00 18.67 18.33 18.00 17.67 17.33 17.00

19.38 19.07 18.77 18.46 18.16 17.86 17.55 17.25 16.96 16.66

18.90 18.61 18.33 18.04 17.76 17.48 17.20 16.92 16.65 16.37

18.67 18.39 18.11 17.84 17.56 17.29 17.03 16.76 16.49 16.23

18.54 18.27 18.00 17.73 17.46 17.20 16.94 16.68 16.42 16.16

18.50 18.23 17.96 17.69 17.43 17.17 16.90 16.65 16.39 16.14

18.47 18.20 17.94 17.67 17.41 17.16 16.89 16.63 16.38 16.13

18.46 18.19 17.92 17.66 17.40 17.14 16.88 16.62 16.37 16.12

18.45 18.18 17.92 17.65 17.39 17.13 16.88 16.62 16.37 16.12

18.43 18.17 17.90 17.64 17.38 17.12 16.87 16.61 16.36 16.12

18.42 18.16 17.89 17.63 17.37 17.12 16.86 16.61 16.36 16.11

18.42 18.15 17.89 17.63 17.37 17.11 16.86 16.61 16.36 16.11

18.41 18.15 17.89 17.62 17.37 17.11 16.86 16.61 16.36 16.11

18.41 18.15 17.88 17.62 17.36 17.11 16.86 16.60 16.36 16.11

1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09

16.67 16.11 15.53 14.93 14.31 13.66 12.98 12.27 11.51 10.71

16.67 16.33 16.00 15.67 15.33 15.00 14.67 14.33 14.00 13.67

16.36 16.07 15.78 15.48 15.19 14.91 14.62 14.33 14.05 13.76

16.10 15.83 15.56 15.30 15.03 14.77 14.51 14.26 14.00 13.75

15.97 15.72 15.46 15.21 14.96 14.71 14.46 14.22 13.97 13.73

15.91 15.66 15.41 15.17 14.92 14.68 14.44 14.20 13.97 13.74

15.89 15.64 15.40 15.15 14.91 14.67 14.44 14.20 13.97 13.74

15.88 15.63 15.39 15.15 14.91 14.67 14.44 14.21 13.98 13.75

15.88 15.63 15.39 15.15 14.91 14.67 14.44 14.21 13.98 13.75

15.87 15.63 15.38 15.15 14.91 14.67 14.44 14.21 13.98 13.76

15.87 15.63 15.38 15.15 14.91 14.68 14.45 14.22 13.99 13.77

15.87 15.62 15.38 15.15 14.91 14.68 14.45 14.22 13.99 13.77

15.87 15.62 15.38 15.15 14.91 14.68 14.45 14.22 14.00 13.77

15.87 15.62 15.39 15.15 14.91 14.68 14.45 14.23 14.00 13.78

15.87 15.62 15.39 15.15 14.91 14.68 14.45 14.22 14.00 13.78

Sample Size

Figure 8 Continued




QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19

9.84 8.89 7.82 6.60 5.08 2.87 0.00 0.00 0.00 0.00

13.33 13.00 12.67 12.33 12.00 11.67 11.33 11.00 10.67 10.33

13.48 13.20 12.93 12.65 12.37 12.10 11.83 11.56 11.29 11.02

13.49 13.25 13.00 12.75 12.51 12.27 12.03 11.79 11.56 11.33

13.50 13.26 13.03 12.80 12.57 12.34 12.12 11.90 11.68 11.46

13.51 13.28 13.05 12.83 12.61 12.39 12.18 11.96 11.75 11.54

13.52 13.29 13.07 12.85 12.63 12.42 12.21 12.00 11.79 11.58

13.52 13.30 13.08 12.86 12.65 12.44 12.22 12.02 11.81 11.61

13.53 13.31 13.09 12.87 12.66 12.45 12.24 12.03 11.82 11.62

13.54 13.31 13.10 12.88 12.67 12.46 12.25 12.04 11.84 11.63

13.54 13.32 13.11 12.89 12.68 12.47 12.26 12.06 11.85 11.65

13.55 13.33 13.12 12.90 12.69 12.48 12.28 12.07 11.87 11.67

13.55 13.34 13.12 12.91 12.70 12.49 12.28 12.08 11.88 11.68

13.56 13.34 13.13 12.91 12.70 12.49 12.29 12.09 11.88 11.69

13.56 13.34 13.13 12.92 12.71 12.50 12.29 12.09 11.89 11.69

1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29

0.00 10.00 10.76 11.10 11.24 11.34 11.38 11.41 11.42 0.00 9.67 10.50 10.87 11.03 11.13 11.18 11.21 11.22 0.00 9.33 10.23 10.65 10.82 10.93 10.98 11.01 11.03 0.00 9.00 9.97 10.42 10.61 10.73 10.78 10.81 10.84 0.00 8.67 9.72 10.20 10.41 10.53 10.59 10.62 10.64 0.00 8.33 9.46 9.98 10.21 10.34 10.40 10.43 10.46 0.00 8.00 9.21 9.77 10.00 10.15 10.21 10.25 10.27 0.00 7.67 8.96 9.55 9.81 9.96 10.02 10.06 10.09 0.00 7.33 8.71 9.34 9.61 9.77 9.84 9.88 9.90 0.00 7.00 8.46 9.13 9.42 9.58 9.66 9.70 9.72

11.43 11.24 11.04 10.85 10.66 10.47 10.29 10.10 9.92 9.74

11.46 11.26 11.07 10.88 10.69 10.50 10.32 10.13 9.95 9.78

11.47 11.28 11.09 10.90 10.71 10.52 10.34 10.16 9.98 9.80

11.48 11.29 11.09 10.91 10.72 10.53 10.35 10.17 9.99 9.82

11.49 11.30 11.10 10.92 10.73 10.54 10.36 10.18 10.00 9.83

11.49 11.30 11.11 10.92 10.73 10.55 10.37 10.19 10.01 9.83

1.30 1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 1.39

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6.67 6.33 6.00 5.67 5.33 5.00 4.67 4.33 4.00 3.67

8.21 7.97 7.73 7.49 7.25 7.02 6.79 6.56 6.33 6.10

8.93 8.72 8.52 8.32 8.12 7.92 7.73 7.54 7.35 7.17

9.22 9.03 8.85 8.66 8.48 8.30 8.12 7.95 7.77 7.60

9.40 9.22 9.04 8.86 8.69 8.52 8.35 8.18 8.01 7.85

9.48 9.30 9.12 8.95 8.78 8.61 8.44 8.28 8.12 7.96

9.52 9.34 9.17 9.00 8.83 8.66 8.50 8.33 8.17 8.01

9.55 9.37 9.20 9.03 8.86 8.69 8.53 8.37 8.21 8.05

9.57 9.39 9.22 9.05 8.88 8.72 8.55 8.39 8.24 8.08

9.60 9.43 9.26 9.09 8.92 8.76 8.60 8.44 8.28 8.12

9.63 9.46 9.29 9.12 8.95 8.79 8.63 8.47 8.31 8.16

9.64 9.47 9.30 9.13 8.97 8.81 8.65 8.49 8.33 8.18

9.65 9.48 9.31 9.15 8.98 8.82 8.66 8.50 8.35 8.19

9.66 9.49 9.32 9.15 8.99 8.83 8.67 8.51 8.36 8.20

1.40 1.41 1.42 1.43 1.44 1.45 1.46 1.47 1.48 1.49

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.33 3.00 2.67 2.33 2.00 1.67 1.33 1.00 .67 .33

5.88 5.66 5.44 5.23 5.02 4.81 4.60 4,39 4.19 3.99

6.98 6.80 6.62 6.45 6.27 6.10 5.93 5.77 5.60 5.44

7.44 7.27 7.10 6.94 6.78 6.63 6.47 6.32 6.17 6.02

7.69 7.53 7.37 7.22 7.07 6.92 6.77 6.63 6.48 6.34

7.80 7.64 7.49 7.34 7.19 7.04 6.90 6.75 6.61 6.48

7.86 7.70 7.55 7.40 7.26 7.11 6.97 6.83 6.69 6.55

7.90 7.74 7.59 7.44 7.30 7.15 7.01 6.87 6.73 6.60

7.92 7.77 7.62 7.47 7.33 7.18 7.04 6.90 6.77 6.63

7.97 7.82 7.67 7.52 7.38 7.24 7.10 6.96 6.82 6.69

8.01 7.86 7.71 7.56 7.42 7.28 7.14 7.00 6.86 6.73

8.02 7.87 7.73 7.58 7.44 7.30 7.16 7.02 6.88 6.75

8.04 7.89 7.74 7.60 7.46 7.32 7.18 7.04 6.90 6.77

8.05 7.90 7.75 7.61 7.47 7.32 7.19 7.05 6.91 6.78

Sample Size

Figure 8 Continued



13

QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

1.50 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.80 3.61 3.42 3.23 3.05 2.87 2.69 2.52 2.35 2.19

5.28 5.13 4.97 4.82 4.67 4.52 4.38 4.24 4.10 3.96

5.87 5.73 5.59 5.45 5.31 5.18 5.05 4.92 4.79 4.66

6.20 6.06 5.93 5.80 5.67 5.54 5.41 5.29 5.16 5.04

6.34 6.20 6.07 5.94 5.81 5.69 5.56 5.44 5.32 5.20

6.41 6.28 6.15 6.02 5.89 5.77 5.65 5.53 5.41 5.29

6.46 6.33 6.20 6.07 5.95 5.82 5.70 5.58 5.46 5.34

6.50 6.36 6.23 6.11 5.98 5.86 5.74 5.62 5.50 5.38

6.55 6.42 6.29 6.17 6.04 5.92 5.80 5.68 5.56 5.45

6.60 6.47 6.34 6.21 6.09 5.97 5.85 5.73 5.61 5.50

6.62 6.49 6.36 6.24 6.11 5.99 5.87 5.75 5.64 5.52

6.64 6.51 6.38 6.26 6.13 6.01 5.89 5.78 5.66 5.55

6.65 6.52 6.39 6.27 6.15 6.02 5.90 5.79 5.67 5.56

1.60 1.61 1.62 1.63 1.64 1.65 1.66 1.67 1.68 1.69

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.03 1.87 1.72 1.57 1.42 1.28 1.15 1.02 0.89 0.77

3.83 3.69 3.57 3.44 3.31 3.19 3.07 2.95 2.84 2.73

4.54 4.41 4.30 4.18 4.06 3.95 3.84 3.73 3.62 3.52

4.92 4.81 4.69 4.58 4.47 4.36 4.25 4.15 4.05 3.94

5.08 4.97 4.86 4.75 4.64 4.53 4.43 4.32 4.22 4.12

5.17 5.06 4.95 4.84 4.73 4.62 4.52 4.42 4.32 4.22

5.23 5.12 5.01 4.90 4.79 4.68 4.58 4.48 4.38 4.28

5.27 5.16 5.04 4.94 4.83 4.72 4.62 4.52 4.42 4.32

5.33 5.22 5.11 5.01 4.90 4.79 4.69 4.59 4.49 4.39

5.38 5.27 5.16 5.06 4.95 4.85 4.74 4.64 4.55 4.45

5.41 5.30 5.19 5.08 4.98 4.87 4.77 4.67 4.57 4.47

5.43 5.32 5.21 5.11 5.00 4.90 4.80 4.70 4.60 4.50

5.44 5.33 5.23 5.12 5.01 4.91 4.81 4.71 4.61 4.51

1.70 1.71 1.72 1.73 1.74 1.75 1.76 1.77 1.78 1.79

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.66 0.55 0.45 0.36 0.27 0.19 0.12 0.06 0.02 0.00

2.62 2.51 2.41 2.30 2.20 2.11 2.01 1.92 1.83 1.74

3.41 3.31 3.21 3.11 3.02 2.93 2.83 2.74 2.66 2.57

3.84 3.75 3.65 3.56 3.46 3.37 3.28 3.20 3.11 3.03

4.02 3.93 3.83 3.74 3.65 3.56 3.47 3.38 3.30 3.21

4.12 4.02 3.93 3.84 3.75 3.66 3.57 3.48 3.40 3.32

4.18 4.09 3.99 3.90 3.81 3.72 3.63 3.55 3.47 3.38

4.22 4.13 4.04 3.94 3.85 3.77 3.68 3.59 3.51 3.43

4.30 4.20 4.11 4.02 3.93 3.84 3.76 3.67 3.59 3.51

4.35 4.26 4.17 4.08 3.99 3.90 3.81 3.73 3.64 3.56

4.38 4.29 4.19 4.10 4.01 3.93 3.84 3.76 3.67 3.59

4.41 4.31 4.22 4.13 4.04 3.95 3.87 3.78 3.70 3.62

4.42 4.32 4.23 4.14 4.05 3.97 3.88 3.80 3.71 3.63

1.80 1.81 1.82 1.83 1.84 1.85 1.86 1.87 1.88 1.89

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.65 1.57 1.49 1.41 1.34 1.26 1.19 1.12 1.06 0.99

2.49 2.40 2.32 2.25 2.17 2.09 2.02 1.95 1.88 1.81

2.94 2.86 2.79 2.71 2.63 2.56 2.48 2.41 2.34 2.28

3.13 3.05 2.98 2.90 2.82 2.75 2.68 2.61 2.54 2.47

3.24 31.6 3.08 3.00 2.93 2.85 2.78 2.71 2.64 2.57

3.30 3.22 3.15 3.07 2.99 2.92 2.85 2.78 2.71 2.64

3.35 3.27 3.19 3.11 3.04 2.97 2.89 2.82 2.75 2.69

3.43 3.35 3.27 3.19 3.12 3.05 2.97 2.90 2.83 2.77

3.48 3.40 3.33 3.25 3.18 3.10 3.03 2.96 2.89 2.83

3.51 3.43 3.36 3.28 3.21 3.13 3.06 2.99 2.92 2.85

3.54 3.46 3.38 3.31 3.23 3.16 3.09 3.02 2.95 2.88

3.55 3.47 3.40 3.32 3.25 3.17 3.10 3.03 2.96 2.90

Sample Size

Figure 8 Continued



QU or QL


Sample Size 3

10

15

20

25

30

35

50

75

100

150

200

1.90 1.91 1.92 1.93 1.94 1.95 1.96 1.97 1.98 1.99

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5

0.93 0.87 0.81 0.76 0.70 0.65 0.60 0.56 0.51 0.47

7

1.75 1.68 1.62 1.56 1.50 1.44 1.38 1.33 1.27 1.22

2.21 2.14 2.08 2.02 1.96 1.90 1.84 1.78 1.73 1.67

2.40 2.34 2.27 2.21 2.15 2.09 2.03 1.97 1.92 1.86

2.51 2.44 2.38 2.32 2.25 2.19 2.14 2.08 2.02 1.97

2.57 2.51 2.45 2.38 2.32 2.26 2.20 2.14 2.09 2.03

2.62 2.56 2.49 2.43 2.37 2.31 2.25 2.19 2.13 2.08

2.70 2.63 2.57 2.51 2.45 2.39 2.33 2.27 2.21 2.16

2.76 2.69 2.63 2.57 2.51 2.45 2.39 2.33 2.27 2.22

2.79 2.72 2.66 2.60 2.54 2.48 2.42 2.36 2.30 2.25

2.82 2.75 2.69 2.63 2.56 2.50 2.44 2.39 2.33 2.27

2.83 2.77 2.70 2.64 2.58 2.52 2.46 2.40 2.34 2.29

2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.43 0.39 0.36 0.32 0.29 0.26 0.23 0.21 0.18 0.16

1.17 1.12 1.07 1.03 0.98 0.94 0.90 0.86 0.82 0.78

1.62 1.57 1.52 1.47 1.42 1.37 1.33 1.28 1.24 1.20

1.81 1.76 1.71 1.66 1.61 1.56 1.51 1.47 1.42 1.38

1.91 1.86 1.81 1.76 1.71 1.66 1.61 1.57 1.52 1.48

1.98 1.93 1.87 1.82 1.77 1.73 1.68 1.63 1.59 1.54

2.03 1.97 1.92 1.87 1.82 1.77 1.72 1.68 1.63 1.59

2.10 2.05 2.00 1.95 1.90 1.85 1.80 1.76 1.71 1.66

2.16 2.11 2.06 2.01 1.96 1.91 1.86 1.81 1.77 1.72

2.19 2.14 2.09 2.04 1.99 1.94 1.89 1.84 1.79 1.75

2.22 2.17 2.11 2.06 2.01 1.96 1.92 1.87 1.82 1.78

2.23 2.18 2.13 2.08 2.03 1.98 1.93 1.88 1.84 1.79

2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.14 0.12 0.10 0.08 0.07 0.06 0.05 0.04 0.03 0.02

0.74 0.71 0.67 0.64 0.61 0.58 0.55 0.52 0.49 0.46

1.16 1.12 1.08 1.04 1.00 0.97 0.93 0.90 0.87 0.83

1.34 1.30 1.26 1.22 1.18 1.14 1.10 1.07 1.03 1.00

1.44 1.39 1.35 1.31 1.28 1.24 1.20 1.16 1.13 1.09

1.50 1.46 1.42 1.38 1.34 1.30 1.26 1.22 1.19 1.15

1.54 1.50 1.46 1.42 1.38 1.34 1.30 1.27 1.23 1.20

1.62 1.58 1.54 1.50 1.46 1.42 1.38 1.34 1.30 1.27

1.68 1.63 1.59 1.55 1.51 1.47 1.43 1.40 1.36 1.32

1.71 1.66 1.62 1.58 1.54 1.50 1.46 1.42 1.39 1.35

1.73 1.69 1.65 1.61 1.57 1.53 1.49 1.45 1.41 1.38

1.75 1.70 1.66 1.62 1.58 1.54 1.50 1.46 1.42 1.39

2.20 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.015 0.010 0.006 0.003 0.002 0.001 0.000 0.000 0.000 0.000

0.437 0.413 0.389 0.366 0.345 0.324 0.304 0.285 0.267 0.250

0.803 0.772 0.734 0.715 0.687 0.660 0.634 0.609 0.585 0.561

0.968 0.936 0.905 0.874 0.845 0.816 0.789 0.762 0.735 0.710

1.160 1.028 0.996 0.965 0.935 0.905 0.876 0.848 0.821 0.794

1.120 1.087 1.054 1.023 0.992 0.962 0.933 0.904 0.876 0.849

1.160 1.128 1.095 1.063 1.032 1.002 0.972 0.943 0.915 0.887

1.233 1.199 1.166 1.134 1.102 1.071 1.041 1.011 0.982 0.954

1.287 1.253 1.219 1.186 1.154 1.123 1.092 1.062 1.033 1.004

1.314 1.279 1.245 1.212 1.180 1.148 1.117 1.087 1.058 1.029

1.340 1.305 1.271 1.238 1.205 1.173 1.142 1.112 1.082 1.053

1.352 1.318 1.284 1.250 1.218 1.186 1.155 1.124 1.095 1.065

Figure 8 Continued



15

QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.233 0.218 0.203 0.189 0.175 0.163 0.151 0.139 0.128 0.118

0.538 0.516 0.495 0.474 0.454 0.435 0.416 0.398 0.381 0.364

0.685 0.661 0.637 0.614 0.592 0.571 0.550 0.530 0.510 0.491

0.769 0.743 0.719 0.695 0.672 0.650 0.628 0.606 0.586 0.566

0.823 0.797 0.772 0.748 0.724 0.701 0.678 0.656 0.635 0.614

0.861 0.834 0.809 0.784 0.760 0.736 0.714 0.691 0.670 0.648

0.927 0.900 0.874 0.848 0.824 0.799 0.776 0.753 0.730 0.709

0.977 0.949 0.923 0.897 0.872 0.847 0.823 0.799 0.777 0.754

1.001 0.974 0.947 0.921 0.895 0.870 0.846 0.822 0.799 0.777

1.025 0.998 0.971 0.944 0.919 0.893 0.869 0.845 0.822 0.799

1.037 1.009 0.982 0.956 0.930 0.905 0.880 0.856 0.833 0.810

2.40 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.109 0.100 0.091 0.083 0.076 0.069 0.063 0.057 0.051 0.046

0.348 0.332 0.317 0.302 0.288 0.275 0.262 0.249 0.237 0.226

0.473 0.455 0.437 0.421 0.404 0.389 0.373 0.359 0.345 0.331

0.546 0.527 0.509 0.491 0.474 0.457 0.440 0.425 0.409 0.394

0.594 0.575 0.555 0.537 0.519 0.501 0.484 0.468 0.452 0.436

0.628 0.608 0.588 0.569 0.551 0.533 0.516 0.499 0.482 0.466

0.687 0.667 0.646 0.627 0.608 0.589 0.571 0.553 0.536 0.519

0.732 0.711 0.691 0.670 0.651 0.632 0.613 0.595 0.577 0.560

0.755 0.733 0.712 0.692 0.672 0.653 0.634 0.615 0.597 0.580

0.777 0.755 0.734 0.713 0.693 0.673 0.654 0.636 0.617 0.600

0.787 0.766 0.744 0.724 0.703 0.684 0.664 0.646 0.627 0.609

2.50 2.51 2.52 2.53 2.54 2.55 2.56 2.57 2.58 2.59

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.041 0.037 0.033 0.029 0.026 0.023 0.020 0.017 0.015 0.013

0.214 0.204 0.193 0.184 0.174 0.165 0.156 0.148 0.140 0.133

0.317 0.305 0.292 0.280 0.268 0.257 0.246 0.236 0.226 0.216

0.380 0.366 0.352 0.339 0.326 0.314 0.302 0.291 0.279 0.269

0.421 0.407 0.392 0.379 0.365 0.352 0.340 0.327 0.316 0.304

0.451 0.436 0.421 0.407 0.393 0.379 0.366 0.354 0.341 0.330

0.503 0.487 0.472 0.457 0.442 0.428 0.414 0.401 0.388 0.375

0.543 0.527 0.511 0.495 0.480 0.465 0.451 0.437 0.424 0.410

0.563 0.546 0.530 0.514 0.499 0.484 0.469 0.455 0.441 0.428

0.582 0.565 0.549 0.533 0.517 0.502 0.487 0.473 0.459 0.445

0.592 0.575 0.559 0.542 0.527 0.511 0.496 0.482 0.468 0.454

2.60 2.61 2.62 2.63 2.64 2.65 2.66 2.67 2.68 2.69

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.011 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 0.002

0.125 0.118 0.112 0.105 0.099 0.094 0.088 0.083 0.078 0.073

0.207 0.198 0.189 0.181 0.172 0.165 0.157 0.150 0.143 0.136

0.258 0.248 0.238 0.229 0.220 0.211 0.202 0.194 0.186 0.179

0.293 0.282 0.272 0.262 0.252 0.242 0.233 0.224 0.216 0.208

0.318 0.307 0.296 0.285 0.275 0.265 0.256 0.246 0.237 0.229

0.363 0.351 0.339 0.328 0.317 0.307 0.296 0.286 0.277 0.267

0.398 0.385 0.373 0.361 0.350 0.339 0.328 0.317 0.307 0.297

0.415 0.402 0.390 0.378 0.366 0.355 0.344 0.333 0.322 0.312

0.432 0.419 0.406 0.394 0.382 0.371 0.359 0.348 0.338 0.327

0.441 0.428 0.415 0.402 0.390 0.379 0.367 0.356 0.345 0.335

Sample Size

Figure 8 Continued




QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

2.70 2.71 2.72 2.73 2.74 2.75 2.76 2.77 2.78 2.79

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000

0.069 0.064 0.060 0.057 0.053 0.049 0.046 0.043 0.040 0.037

0.130 0.124 0.118 0.112 0.107 0.102 0.097 0.092 0.087 0.083

0.171 0.164 0.157 0.151 0.144 0.138 0.132 0.126 0.121 0.115

0.200 0.192 0.184 0.177 0.170 0.163 0.157 0.151 0.145 0.139

0.220 0.212 0.204 0.197 0.189 0.182 0.175 0.168 0.162 0.156

0.258 0.249 0.241 0.232 0.224 0.216 0.209 0.201 0.194 0.187

0.288 0.278 0.269 0.260 0.252 0.243 0.235 0.227 0.220 0.212

0.302 0.293 0.283 0.274 0.266 0.257 0.249 0.241 0.223 0.220

0.317 0.307 0.298 0.288 0.279 0.271 0.262 0.254 0.246 0.238

0.325 0.315 0.305 0.296 0.286 0.277 0.269 0.260 0.252 0.244

2.80 2.81 2.82 2.83 2.84 2.85 2.86 2.87 2.88 2.89

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.035 0.032 0.030 0.028 0.026 0.024 0.022 0.020 0.019 0.017

0.079 0.075 0.071 0.067 0.064 0.060 0.057 0.054 0.051 0.048

0.110 0.105 0.101 0.096 0.092 0.088 0.084 0.080 0.076 0.073

0.133 0.128 0.122 0.117 0.112 0.108 0.103 0.099 0.094 0.090

0.150 0.144 0.138 0.133 0.128 0.122 0.118 0.113 0.108 0.104

0.181 0.174 0.168 0.162 0.156 0.150 0.145 0.139 0.134 0.129

0.205 0.198 0.192 0.185 0.179 0.173 0.167 0.161 0.155 0.150

0.218 0.211 0.204 0.197 0.190 0.184 0.178 0.172 0.166 0.160

0.230 0.223 0.216 0.209 0.202 0.195 0.189 0.183 0.177 0.171

0.237 0.229 0.222 0.215 0.208 0.201 0.195 0.188 0.182 0.176

2.90 2.91 2.92 2.93 2.94 2.95 2.96 2.97 2.98 2.99

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.016 0.015 0.013 0.012 0.011 0.010 0.009 0.009 0.008 0.007

0.046 0.043 0.041 0.038 0.036 0.034 0.032 0.030 0.028 0.027

0.069 0.066 0.063 0.060 0.057 0.054 0.051 0.049 0.046 0.044

0.087 0.083 0.079 0.076 0.072 0.069 0.066 0.063 0.060 0.057

0.100 0.096 0.092 0.088 0.084 0.081 0.077 0.074 0.071 0.068

0.125 0.120 0.115 0.111 0.107 0.103 0.099 0.095 0.091 0.088

0.145 0.140 0.135 0.130 0.125 0.121 0.117 0.112 0.108 0.104

0.155 0.150 0.145 0.140 0.135 0.130 0.126 0.121 0.117 0.113

0.165 0.160 0.155 0.149 0.144 0.140 0.135 0.130 0.126 0.122

0.171 0.165 0.160 0.154 0.149 0.144 0.140 0.135 0.130 0.126

3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.006 0.006 0.005 0.005 0.004 0.004 0.003 0.003 0.003 0.002

0.025 0.024 0.022 0.021 0.019 0.018 0.017 0.016 0.015 0.014

0.042 0.040 0.038 0.036 0.034 0.032 0.030 0.029 0.027 0.026

0.055 0.052 0.050 0.048 0.045 0.043 0.041 0.039 0.037 0.036

0.065 0.062 0.059 0.057 0.054 0.052 0.050 0.047 0.045 0.043

0.084 0.081 0.078 0.075 0.072 0.069 0.066 0.064 0.061 0.059

0.101 0.097 0.093 0.090 0.087 0.083 0.080 0.077 0.074 0.072

0.109 0.105 0.101 0.098 0.094 0.091 0.088 0.085 0.081 0.079

0.118 0.113 0.110 0.106 0.102 0.099 0.095 0.092 0.089 0.086

0.122 0.118 0.114 0.110 0.106 0.103 0.099 0.096 0.092 0.089

Sample Size

Figure 8 Continued



17

QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001

0.013 0.012 0.011 0.011 0.010 0.009 0.009 0.008 0.007 0.007

0.024 0.023 0.022 0.021 0.019 0.018 0.017 0.016 0.015 0.015

0.034 0.032 0.031 0.029 0.028 0.026 0.025 0.024 0.022 0.021

0.041 0.039 0.038 0.036 0.034 0.033 0.031 0.030 0.028 0.027

0.056 0.054 0.052 0.050 0.048 0.046 0.044 0.042 0.040 0.038

0.069 0.066 0.064 0.061 0.059 0.057 0.055 0.053 0.050 0.049

0.076 0.073 0.070 0.068 0.065 0.063 0.060 0.058 0.056 0.054

0.083 0.080 0.077 0.074 0.071 0.069 0.066 0.064 0.062 0.059

0.086 0.083 0.080 0.077 0.075 0.072 0.069 0.067 0.065 0.062

3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.006 0.006 0.005 0.005 0.005 0.004 0.004 0.004 0.003 0.003

0.014 0.013 0.012 0.011 0.011 0.010 0.009 0.009 0.008 0.008

0.020 0.019 0.018 0.017 0.016 0.015 0.015 0.014 0.013 0.012

0.026 0.024 0.023 0.022 0.021 0.020 0.019 0.018 0.017 0.016

0.037 0.035 0.034 0.032 0.031 0.030 0.028 0.027 0.026 0.025

0.047 0.045 0.043 0.041 0.040 0.038 0.037 0.035 0.034 0.032

0.052 0.050 0.048 0.046 0.044 0.043 0.042 0.040 0.038 0.037

0.057 0.055 0.053 0.051 0.049 0.048 0.046 0.044 0.042 0.041

0.060 0.058 0.056 0.054 0.052 0.050 0.048 0.046 0.045 0.043

3.30 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.003 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.001 0.001

0.007 0.007 0.006 0.006 0.006 0.005 0.005 0.005 0.004 0.004

0.012 0.011 0.010 0.010 0.009 0.009 0.008 0.008 0.007 0.007

0.015 0.015 0.014 0.013 0.013 0.012 0.011 0.011 0.010 0.010

0.024 0.023 0.022 0.021 0.020 0.019 0.018 0.017 0.016 0.016

0.031 0.030 0.029 0.027 0.026 0.025 0.024 0.023 0.022 0.021

0.035 0.034 0.032 0.031 0.030 0.029 0.028 0.026 0.025 0.024

0.039 0.038 0.036 0.035 0.034 0.032 0.031 0.030 0.029 0.028

0.042 0.040 0.038 0.037 0.036 0.034 0.033 0.032 0.031 0.029

3.40 3.41 3.42 3.43 3.44 3.45 3.46 3.47 3.48 3.49

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.000

0.004 0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002

0.007 0.006 0.006 0.005 0.005 0.005 0.005 0.004 0.004 0.004

0.009 0.009 0.008 0.008 0.007 0.007 0.007 0.006 0.006 0.005

0.015 0.014 0.014 0.013 0.012 0.012 0.011 0.011 0.010 0.010

0.020 0.020 0.019 0.018 0.017 0.016 0.016 0.015 0.014 0.014

0.023 0.022 0.022 0.021 0.020 0.019 0.018 0.018 0.017 0.016

0.027 0.026 0.025 0.024 0.023 0.022 0.021 0.020 0.019 0.019

0.028 0.027 0.026 0.025 0.024 0.023 0.022 0.022 0.021 0.020

Sample Size

Figure 8 Continued




QU or QL

3

4

5

7

10

15

20

25

30

35

50

75

100

150

200

3.50 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58 3.59

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.002 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.003 0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002

0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.003 0.003 0.003

0.009 0.009 0.008 0.008 0.008 0.007 0.007 0.006 0.006 0.006

0.013 0.013 0.012 0.011 0.011 0.011 0.010 0.010 0.009 0.009

0.015 0.015 0.014 0.014 0.013 0.012 0.012 0.011 0.011 0.010

0.018 0.017 0.016 0.016 0.015 0.015 0.014 0.013 0.013 0.012

0.019 0.018 0.018 0.017 0.016 0.016 0.015 0.014 0.014 0.013

3.60 3.61 3.62 3.63 3.64 3.65 3.66 3.67 3.68 3.69

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000

0.002 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002

0.006 0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.003

0.008 0.008 0.008 0.007 0.007 0.007 0.006 0.006 0.006 0.005

0.010 0.010 0.009 0.009 0.008 0.008 0.008 0.007 0.007 0.007

0.012 0.011 0.011 0.010 0.010 0.010 0.009 0.009 0.008 0.008

0.013 0.012 0.012 0.011 0.011 0.010 0.010 0.010 0.009 0.009

3.70 3.71 3.72 3.73 3.74 3.75 3.76 3.77 3.78 3.79

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000

0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.002

0.005 0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.003

0.006 0.006 0.006 0.006 0.005 0.005 0.005 0.005 0.004 0.004

0.008 0.007 0.007 0.007 0.006 0.006 0.006 0.006 0.005 0.005

0.008 0.008 0.008 0.007 0.007 0.007 0.007 0.006 0.006 0.006

3.80 3.81 3.82 3.83 3.84 3.85 3.86 3.87 3.88 3.89 3.90

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000

0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.002 0.002

0.004 0.004 0.004 0.004 0.003 0.003 0.003 0.003 0.003 0.003 0.003

0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.003 0.003 0.003

0.006 0.005 0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.004

Sample Size

Figure 8 Continued




Sample Size Code Letter

Acceptance Quality Limits (normal inspection) Sample Size

B

3

C

4

D

5

E

7

F

10

G H

T

.10

.15

.25

.40

.65

1.00

1.50

2.50

4.00

M

M

M

M

M

M

M

M

M

M

6.50 10.00 M

M

7.59 18.86 26.94 33.69 1.49

5.46 10.88 16.41 22.84 29.43

0.041 1.34 3.33

5.82

9.80 14.37 20.19 26.55

0.005 0.087 0.421 1.05

2.13 3.54

5.34

8.40 12.19 17.34 23.30

0.077

0.179 0.349 0.714 1.27

2.14 3.27

4.72

7.26 10.53 15.17 20.73

15

0.186

0.311 0.491 0.839 1.33

2.09 3.06

4.32

6.55

9.48 13.74 18.97

20

0.228

0.356 0.531 0.864 1.33

2.03 2.93

4.10

6.18

8.95 13.01 18.07

I

25

0.250

0.378 0.551 0.874 1.32

2.00 2.86

3.97

5.98

8.65 12.60 17.55

J

35

0.253

0.373 0.534 0.833 1.24

1.87 2.66

3.70

5.58

8.11 11.89 16.67

K

50

0.243

0.355 0.503 0.778 1.16

1.73 2.47

3.44

5.21

7.61 11.23 15.87

L

75

0.225

0.326 0.461 0.711 1.06

1.59 2.27

3.17

4.83

7.10 10.58 15.07

M

100

0.218

0.315 0.444 0.684 1.02

1.52 2.18

3.06

4.67

6.88 10.29 14.71

N

150

0.202

0.292 0.412 0.636 0.946 1.42 2.05

2.88

4.42

6.56

9.86 14.18

P

200

0.204

0.294 0.414 0.637 0.945 1.42 2.04

2.86

4.39

6.52

9.80 14.11

1.00 1.50

2.50

4.00

6.50 10.00

.10

.15

.25

.40

.65

Acceptance Quality Limits (tightened inspection) All AQL values are in percent nonconforming. T denotes plan used exclusively on tightened inspection and provides symbol for identification of appropriate OC curve. Use first sampling plan below arrow; that is, both sample size as well as k value. When sample size equals or exceeds lot size, every item in the lot must be inspected.

Figure 9 ANSI/ASQ Z1.9-2003 Table B-3: Master table for normal and tightened inspection for plans based on variability unknown (double specification limit and form 2— single specification limit).


Calidad de Ingenieria Cqe Hb 3e e1340

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